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8eded16b4bca218dc0e5939cce56103ed3800a6a |
# Dataset Card for road-traffic
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/road-traffic
- **Point of Contact:** [email protected]
### Dataset Summary
road-traffic
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/road-traffic
### Citation Information
```
@misc{ road-traffic,
title = { road traffic Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/road-traffic } },
url = { https://universe.roboflow.com/object-detection/road-traffic },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/road-traffic | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:50+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "road-traffic", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "road-traffic", "1": "bicycles", "2": "buses", "3": "crosswalks", "4": "fire hydrants", "5": "motorcycles", "6": "traffic lights", "7": "vehicles"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:12:18+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for road-traffic
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
road-traffic
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for road-traffic\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nroad-traffic",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for road-traffic\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nroad-traffic",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for road-traffic\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nroad-traffic### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
989f5749f40823b3865aa4185f8d44ce69371784 |
# Dataset Card for bees-jt5in
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/bees-jt5in
- **Point of Contact:** [email protected]
### Dataset Summary
bees-jt5in
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bees-jt5in
### Citation Information
```
@misc{ bees-jt5in,
title = { bees jt5in Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bees-jt5in } },
url = { https://universe.roboflow.com/object-detection/bees-jt5in },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/bees-jt5in | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:51+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "bees-jt5in", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "bees-0", "1": "bees"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:14:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for bees-jt5in
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
bees-jt5in
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for bees-jt5in\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbees-jt5in",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for bees-jt5in\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbees-jt5in",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for bees-jt5in\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nbees-jt5in### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
5f996521a6bfdd65fb2a0fc5fe1ffcbb4207f70e |
# Dataset Card for aerial-cows
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/aerial-cows
- **Point of Contact:** [email protected]
### Dataset Summary
aerial-cows
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/aerial-cows
### Citation Information
```
@misc{ aerial-cows,
title = { aerial cows Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/aerial-cows } },
url = { https://universe.roboflow.com/object-detection/aerial-cows },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/aerial-cows | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:11:54+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "aerial-cows", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "aerial-cows", "1": "cow"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:12:41+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for aerial-cows
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
aerial-cows
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for aerial-cows\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naerial-cows",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for aerial-cows\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naerial-cows",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
16,
5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for aerial-cows\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\naerial-cows### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
5c0e1d3d0f5a95bb916995c8ad34778cd2ca6c7c |
# Dataset Card for furniture-ngpea
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/furniture-ngpea
- **Point of Contact:** [email protected]
### Dataset Summary
furniture-ngpea
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/furniture-ngpea
### Citation Information
```
@misc{ furniture-ngpea,
title = { furniture ngpea Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/furniture-ngpea } },
url = { https://universe.roboflow.com/object-detection/furniture-ngpea },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/furniture-ngpea | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:12:19+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "furniture-ngpea", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "furniture", "1": "Chair", "2": "Sofa", "3": "Table"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:12:40+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for furniture-ngpea
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
furniture-ngpea
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for furniture-ngpea\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nfurniture-ngpea",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for furniture-ngpea\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nfurniture-ngpea",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for furniture-ngpea\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nfurniture-ngpea### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
558e96d0119d2833f55ad7113021e1df511745ed |
# Dataset Card for thermal-cheetah-my4dp
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp
- **Point of Contact:** [email protected]
### Dataset Summary
thermal-cheetah-my4dp
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp
### Citation Information
```
@misc{ thermal-cheetah-my4dp,
title = { thermal cheetah my4dp Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp } },
url = { https://universe.roboflow.com/object-detection/thermal-cheetah-my4dp },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/thermal-cheetah-my4dp | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:12:40+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "thermal-cheetah-my4dp", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "thermal-cheetah", "1": "cheetah", "2": "human"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:12:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for thermal-cheetah-my4dp
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
thermal-cheetah-my4dp
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for thermal-cheetah-my4dp\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nthermal-cheetah-my4dp",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for thermal-cheetah-my4dp\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nthermal-cheetah-my4dp",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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30,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for thermal-cheetah-my4dp\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nthermal-cheetah-my4dp### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
29888d7b0512bd7f60645f5fdddf9501673fc797 |
# Dataset Card for fish-market-ggjso
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/fish-market-ggjso
- **Point of Contact:** [email protected]
### Dataset Summary
fish-market-ggjso
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/fish-market-ggjso
### Citation Information
```
@misc{ fish-market-ggjso,
title = { fish market ggjso Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/fish-market-ggjso } },
url = { https://universe.roboflow.com/object-detection/fish-market-ggjso },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/fish-market-ggjso | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:12:41+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "fish-market-ggjso", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "fish", "1": "aair", "2": "boal", "3": "chapila", "4": "deshi puti", "5": "foli", "6": "ilish", "7": "kal baush", "8": "katla", "9": "koi", "10": "magur", "11": "mrigel", "12": "pabda", "13": "pangas", "14": "puti", "15": "rui", "16": "shol", "17": "taki", "18": "tara baim", "19": "telapiya"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:34+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for fish-market-ggjso
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
fish-market-ggjso
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for fish-market-ggjso\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nfish-market-ggjso",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for fish-market-ggjso\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nfish-market-ggjso",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
13,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for fish-market-ggjso\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nfish-market-ggjso### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
02a0263ac6694c917c374fa112c4863a0cc0939b |
# Dataset Card for parasites-1s07h
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/parasites-1s07h
- **Point of Contact:** [email protected]
### Dataset Summary
parasites-1s07h
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/parasites-1s07h
### Citation Information
```
@misc{ parasites-1s07h,
title = { parasites 1s07h Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/parasites-1s07h } },
url = { https://universe.roboflow.com/object-detection/parasites-1s07h },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/parasites-1s07h | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:12:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "parasites-1s07h", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "parasites", "1": "Ancylostoma Spp", "2": "Ascaris Lumbricoides", "3": "Enterobius Vermicularis", "4": "Fasciola Hepatica", "5": "Hymenolepis", "6": "Schistosoma", "7": "Taenia Sp", "8": "Trichuris Trichiura"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:13:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for parasites-1s07h
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
parasites-1s07h
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for parasites-1s07h\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nparasites-1s07h",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for parasites-1s07h\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nparasites-1s07h",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
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22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for parasites-1s07h\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nparasites-1s07h### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
afc7570ad69acd1ec488302a219f03de586f3a95 |
# Dataset Card for cells-uyemf
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cells-uyemf
- **Point of Contact:** [email protected]
### Dataset Summary
cells-uyemf
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cells-uyemf
### Citation Information
```
@misc{ cells-uyemf,
title = { cells uyemf Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cells-uyemf } },
url = { https://universe.roboflow.com/object-detection/cells-uyemf },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cells-uyemf | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:13:30+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cells-uyemf", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cells", "1": "celula"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:13:46+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cells-uyemf
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cells-uyemf
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cells-uyemf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncells-uyemf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cells-uyemf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncells-uyemf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
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33,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cells-uyemf\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncells-uyemf### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
07181d7d834b4c6063cdcc3ef7dbbf682cc14c92 |
# Dataset Card for acl-x-ray
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/acl-x-ray
- **Point of Contact:** [email protected]
### Dataset Summary
acl-x-ray
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/acl-x-ray
### Citation Information
```
@misc{ acl-x-ray,
title = { acl x ray Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/acl-x-ray } },
url = { https://universe.roboflow.com/object-detection/acl-x-ray },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/acl-x-ray | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:13:36+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "acl-x-ray", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "acl-x-ray", "1": "acl"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:14:08+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for acl-x-ray
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
acl-x-ray
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for acl-x-ray\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nacl-x-ray",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for acl-x-ray\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nacl-x-ray",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for acl-x-ray\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nacl-x-ray### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
2fe1c8ae95d2a46d8c9568846fedb04b16ab3790 |
# Dataset Card for bccd-ouzjz
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/bccd-ouzjz
- **Point of Contact:** [email protected]
### Dataset Summary
bccd-ouzjz
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bccd-ouzjz
### Citation Information
```
@misc{ bccd-ouzjz,
title = { bccd ouzjz Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bccd-ouzjz } },
url = { https://universe.roboflow.com/object-detection/bccd-ouzjz },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/bccd-ouzjz | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:13:46+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "bccd-ouzjz", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "bccd", "1": "Platelets", "2": "RBC", "3": "WBC"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:14:05+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for bccd-ouzjz
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
bccd-ouzjz
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for bccd-ouzjz\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbccd-ouzjz",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for bccd-ouzjz\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbccd-ouzjz",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for bccd-ouzjz\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nbccd-ouzjz### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
678ef951ad5a7d96fc7a057344e879a97a9f90cd |
# Dataset Card for poker-cards-cxcvz
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/poker-cards-cxcvz
- **Point of Contact:** [email protected]
### Dataset Summary
poker-cards-cxcvz
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/poker-cards-cxcvz
### Citation Information
```
@misc{ poker-cards-cxcvz,
title = { poker cards cxcvz Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/poker-cards-cxcvz } },
url = { https://universe.roboflow.com/object-detection/poker-cards-cxcvz },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/poker-cards-cxcvz | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:14:05+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "poker-cards-cxcvz", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "poker-cards", "1": 59, "2": "10 Diamonds", "3": "10 Hearts", "4": "10 Spades", "5": "10 Trefoils", "6": "2 Diamonds", "7": "2 Hearts", "8": "2 Spades", "9": "2 Trefoils", "10": "3 Diamonds", "11": "3 Hearts", "12": "3 Spades", "13": "3 Trefoils", "14": "4 Diamonds", "15": "4 Hearts", "16": "4 Spades", "17": "4 Trefoils", "18": "5 Diamonds", "19": "5 Hearts", "20": "5 Spades", "21": "5 Trefoils", "22": "6 Diamonds", "23": "6 Hearts", "24": "6 Spades", "25": "6 Trefoils", "26": "7 Diamonds", "27": "7 Hearts", "28": "7 Spades", "29": "7 Trefoils", "30": "8 Diamonds", "31": "8 Hearts", "32": "8 Spades", "33": "8 Trefoils", "34": "9 Diamonds", "35": "9 Hearts", "36": "9 Spades", "37": "9 Trefoils", "38": "A Diamonds", "39": "A Hearts", "40": "A Spades", "41": "A Trefoils", "42": "J Diamonds", "43": "J Hearts", "44": "J Spades", "45": "J Trefoils", "46": "K Diamonds", "47": "K Hearts", "48": "K Spades", "49": "K Trefoils", "50": "Q Diamonds", "51": "Q Hearts", "52": "Q Spades", "53": "Q Trefoils"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:14:35+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for poker-cards-cxcvz
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
poker-cards-cxcvz
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for poker-cards-cxcvz\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npoker-cards-cxcvz",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for poker-cards-cxcvz\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npoker-cards-cxcvz",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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22,
15,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for poker-cards-cxcvz\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npoker-cards-cxcvz### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a06a3254448172ded7e295ee21d3cf87d04b8a49 |
# Dataset Card for truck-movement
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/truck-movement
- **Point of Contact:** [email protected]
### Dataset Summary
truck-movement
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/truck-movement
### Citation Information
```
@misc{ truck-movement,
title = { truck movement Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/truck-movement } },
url = { https://universe.roboflow.com/object-detection/truck-movement },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/truck-movement | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:14:08+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "truck-movement", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "truck-movement", "1": "otr_chassis_loaded", "2": "otr_chassis_unloaded", "3": "otr_chassis_working", "4": "person", "5": "stacker"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:14:40+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for truck-movement
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
truck-movement
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for truck-movement\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntruck-movement",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for truck-movement\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntruck-movement",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for truck-movement\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ntruck-movement### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
c59b6cd19a84115766acfcf79ed6d313165b44aa |
# Dataset Card for digits-t2eg6
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/digits-t2eg6
- **Point of Contact:** [email protected]
### Dataset Summary
digits-t2eg6
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/digits-t2eg6
### Citation Information
```
@misc{ digits-t2eg6,
title = { digits t2eg6 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/digits-t2eg6 } },
url = { https://universe.roboflow.com/object-detection/digits-t2eg6 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/digits-t2eg6 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:14:35+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "digits-t2eg6", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "digits", "1": 0, "2": 1, "3": 2, "4": 3, "5": 4, "6": 5, "7": 6, "8": 7, "9": 8, "10": 9}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:15:27+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for digits-t2eg6
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
digits-t2eg6
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for digits-t2eg6\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ndigits-t2eg6",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for digits-t2eg6\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ndigits-t2eg6",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for digits-t2eg6\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ndigits-t2eg6### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
77e7baefd1ef98310414a260abe13e5706354915 |
# Dataset Card for phages
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/phages
- **Point of Contact:** [email protected]
### Dataset Summary
phages
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/phages
### Citation Information
```
@misc{ phages,
title = { phages Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/phages } },
url = { https://universe.roboflow.com/object-detection/phages },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/phages | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:14:40+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "phages", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "phages", "1": "activated", "2": "non-activated"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:15:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for phages
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
phages
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for phages\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nphages",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for phages\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nphages",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
20,
22,
8,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for phages\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nphages### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
e2d54ecf2ab815773efcdb06ea3bb009d6035dd7 |
# Dataset Card for bone-fracture-7fylg
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/bone-fracture-7fylg
- **Point of Contact:** [email protected]
### Dataset Summary
bone-fracture-7fylg
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bone-fracture-7fylg
### Citation Information
```
@misc{ bone-fracture-7fylg,
title = { bone fracture 7fylg Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bone-fracture-7fylg } },
url = { https://universe.roboflow.com/object-detection/bone-fracture-7fylg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/bone-fracture-7fylg | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:14:40+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "bone-fracture-7fylg", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "bone-fracture", "1": "angle", "2": "fracture", "3": "line", "4": "messed_up_angle"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:14:59+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for bone-fracture-7fylg
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
bone-fracture-7fylg
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for bone-fracture-7fylg\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbone-fracture-7fylg",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for bone-fracture-7fylg\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbone-fracture-7fylg",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for bone-fracture-7fylg\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nbone-fracture-7fylg### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
13c1c1432cdb65d0c1eb1ee49629fcbb6e2f5269 |
# Dataset Card for csgo-videogame
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/csgo-videogame
- **Point of Contact:** [email protected]
### Dataset Summary
csgo-videogame
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/csgo-videogame
### Citation Information
```
@misc{ csgo-videogame,
title = { csgo videogame Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/csgo-videogame } },
url = { https://universe.roboflow.com/object-detection/csgo-videogame },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/csgo-videogame | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:15:12+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "csgo-videogame", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "CSGO", "1": "CT", "2": "T"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:15:55+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for csgo-videogame
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
csgo-videogame
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for csgo-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncsgo-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for csgo-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncsgo-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
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233,
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5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for csgo-videogame\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncsgo-videogame### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
1cedf75b9406833ef6ff00bdee3561fdb0fe841b |
# Dataset Card for team-fight-tactics
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/team-fight-tactics
- **Point of Contact:** [email protected]
### Dataset Summary
team-fight-tactics
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/team-fight-tactics
### Citation Information
```
@misc{ team-fight-tactics,
title = { team fight tactics Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/team-fight-tactics } },
url = { https://universe.roboflow.com/object-detection/team-fight-tactics },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/team-fight-tactics | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:15:16+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "team-fight-tactics", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "characters", "1": "Akali", "2": "Blitzcrank", "3": "Braum", "4": "Caitlyn", "5": "Camille", "6": "Cho-Gath", "7": "Darius", "8": "Dr- Mundo", "9": "Ekko", "10": "Ezreal", "11": "Fiora", "12": "Galio", "13": "Gankplank", "14": "Garen", "15": "Graves", "16": "Heimerdinger", "17": "Illaoi", "18": "Janna", "19": "Jayce", "20": "Jhin", "21": "Jinx", "22": "Kai-Sa", "23": "Kassadin", "24": "Katarina", "25": "Kog-Maw", "26": "Leona", "27": "Lissandra", "28": "Lulu", "29": "Lux", "30": "Malzahar", "31": "Miss Fortune", "32": "Orianna", "33": "Poppy", "34": "Quinn", "35": "Samira", "36": "Seraphine", "37": "Shaco", "38": "Singed", "39": "Sion", "40": "Swain", "41": "Tahm Kench", "42": "Talon", "43": "Taric", "44": "Tristana", "45": "Trundle", "46": "Twisted Fate", "47": "Twitch", "48": "Urgot", "49": "Veigar", "50": "Vex", "51": "Vi", "52": "Viktor", "53": "Warwick", "54": "Yone", "55": "Yuumi", "56": "Zac", "57": "Ziggs", "58": "Zilean", "59": "Zyra"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for team-fight-tactics
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
team-fight-tactics
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for team-fight-tactics\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nteam-fight-tactics",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for team-fight-tactics\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nteam-fight-tactics",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for team-fight-tactics\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nteam-fight-tactics### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
89bb824ede8af3d8df4319ba191ad6f21b15f211 |
# Dataset Card for valentines-chocolate
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/valentines-chocolate
- **Point of Contact:** [email protected]
### Dataset Summary
valentines-chocolate
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/valentines-chocolate
### Citation Information
```
@misc{ valentines-chocolate,
title = { valentines chocolate Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/valentines-chocolate } },
url = { https://universe.roboflow.com/object-detection/valentines-chocolate },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/valentines-chocolate | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:15:30+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "valentines-chocolate", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "valentines-chocolate", "1": "sees-dark-almond-nougat", "2": "sees-dark-almonds", "3": "sees-dark-bordeaux", "4": "sees-dark-caramel-patties", "5": "sees-dark-chocolate-buttercream", "6": "sees-dark-marzipan", "7": "sees-dark-normandie", "8": "sees-dark-scotchmallow", "9": "sees-dark-walnut-square", "10": "sees-milk-almond-caramel", "11": "sees-milk-almonds", "12": "sees-milk-beverly", "13": "sees-milk-bordeaux", "14": "sees-milk-butterscotch-square", "15": "sees-milk-california-brittle", "16": "sees-milk-chelsea", "17": "sees-milk-chocolate-buttercream", "18": "sees-milk-coconut-cream", "19": "sees-milk-mayfair", "20": "sees-milk-mocha", "21": "sees-milk-molasses-chips", "22": "sees-milk-rum-nougat"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:15:50+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for valentines-chocolate
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
valentines-chocolate
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for valentines-chocolate\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nvalentines-chocolate",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for valentines-chocolate\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nvalentines-chocolate",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for valentines-chocolate\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nvalentines-chocolate### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
ce7718b3ba2d133188282d42683f142b5eab0c95 |
# Dataset Card for asbestos
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/asbestos
- **Point of Contact:** [email protected]
### Dataset Summary
asbestos
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/asbestos
### Citation Information
```
@misc{ asbestos,
title = { asbestos Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/asbestos } },
url = { https://universe.roboflow.com/object-detection/asbestos },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/asbestos | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:15:51+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "asbestos", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "asbestos", "1": "thick-dark-mark", "2": "thick-light-mark", "3": "thin-dark-mark", "4": "thin-light-mark"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for asbestos
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
asbestos
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for asbestos\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nasbestos",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for asbestos\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nasbestos",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
21,
22,
9,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for asbestos\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nasbestos### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
0d3447a9b46b4f591a0a2b3aa92878bb282c6f11 |
# Dataset Card for shark-teeth-5atku
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/shark-teeth-5atku
- **Point of Contact:** [email protected]
### Dataset Summary
shark-teeth-5atku
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/shark-teeth-5atku
### Citation Information
```
@misc{ shark-teeth-5atku,
title = { shark teeth 5atku Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/shark-teeth-5atku } },
url = { https://universe.roboflow.com/object-detection/shark-teeth-5atku },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/shark-teeth-5atku | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:15:55+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "shark-teeth-5atku", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "teeth", "1": "Lower", "2": "Sand Tiger Shark", "3": "Snaggletooth Shark", "4": "Upper"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for shark-teeth-5atku
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
shark-teeth-5atku
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for shark-teeth-5atku\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nshark-teeth-5atku",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for shark-teeth-5atku\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nshark-teeth-5atku",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for shark-teeth-5atku\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nshark-teeth-5atku### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
c24e632e7181f02a09eba25aefd1a6c4ad8cbf73 |
# Dataset Card for peixos-fish
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/peixos-fish
- **Point of Contact:** [email protected]
### Dataset Summary
peixos-fish
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/peixos-fish
### Citation Information
```
@misc{ peixos-fish,
title = { peixos fish Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/peixos-fish } },
url = { https://universe.roboflow.com/object-detection/peixos-fish },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/peixos-fish | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:16:13+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "peixos-fish", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "peixos", "1": "peix", "2": "taca"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:57+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for peixos-fish
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
peixos-fish
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for peixos-fish\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npeixos-fish",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for peixos-fish\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npeixos-fish",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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23,
22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for peixos-fish\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npeixos-fish### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
040828dedabe342b06fbddba93ca0d8400358472 |
# Dataset Card for aquarium-qlnqy
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/aquarium-qlnqy
- **Point of Contact:** [email protected]
### Dataset Summary
aquarium-qlnqy
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/aquarium-qlnqy
### Citation Information
```
@misc{ aquarium-qlnqy,
title = { aquarium qlnqy Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/aquarium-qlnqy } },
url = { https://universe.roboflow.com/object-detection/aquarium-qlnqy },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/aquarium-qlnqy | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:16:16+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "aquarium-qlnqy", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "aquarium", "1": "fish", "2": "jellyfish", "3": "penguin", "4": "puffin", "5": "shark", "6": "starfish", "7": "stingray"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:41+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for aquarium-qlnqy
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
aquarium-qlnqy
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for aquarium-qlnqy\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naquarium-qlnqy",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for aquarium-qlnqy\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naquarium-qlnqy",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
16,
5,
11,
17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for aquarium-qlnqy\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\naquarium-qlnqy### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
c8685b5cdb5d505fe9e79286a8c532729bcac470 |
# Dataset Card for vehicles-q0x2v
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/vehicles-q0x2v
- **Point of Contact:** [email protected]
### Dataset Summary
vehicles-q0x2v
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/vehicles-q0x2v
### Citation Information
```
@misc{ vehicles-q0x2v,
title = { vehicles q0x2v Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/vehicles-q0x2v } },
url = { https://universe.roboflow.com/object-detection/vehicles-q0x2v },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/vehicles-q0x2v | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:16:17+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "vehicles-q0x2v", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "vehicles", "1": "big bus", "2": "big truck", "3": "bus-l-", "4": "bus-s-", "5": "car", "6": "mid truck", "7": "small bus", "8": "small truck", "9": "truck-l-", "10": "truck-m-", "11": "truck-s-", "12": "truck-xl-"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:17:19+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for vehicles-q0x2v
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
vehicles-q0x2v
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for vehicles-q0x2v\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nvehicles-q0x2v",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for vehicles-q0x2v\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nvehicles-q0x2v",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for vehicles-q0x2v\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nvehicles-q0x2v### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
dd97f75cd7b06473b857af722230b7e530607b19 |
# Dataset Card for secondary-chains
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/secondary-chains
- **Point of Contact:** [email protected]
### Dataset Summary
secondary-chains
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/secondary-chains
### Citation Information
```
@misc{ secondary-chains,
title = { secondary chains Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/secondary-chains } },
url = { https://universe.roboflow.com/object-detection/secondary-chains },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/secondary-chains | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:16:34+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "secondary-chains", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "secondary-chains", "1": "chain"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:16:54+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for secondary-chains
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
secondary-chains
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for secondary-chains\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsecondary-chains",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for secondary-chains\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsecondary-chains",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
20,
233,
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5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for secondary-chains\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsecondary-chains### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
db4bda29e30d7f56fa7648dd13784ac7acdaf69c |
# Dataset Card for underwater-pipes-4ng4t
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/underwater-pipes-4ng4t
- **Point of Contact:** [email protected]
### Dataset Summary
underwater-pipes-4ng4t
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/underwater-pipes-4ng4t
### Citation Information
```
@misc{ underwater-pipes-4ng4t,
title = { underwater pipes 4ng4t Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/underwater-pipes-4ng4t } },
url = { https://universe.roboflow.com/object-detection/underwater-pipes-4ng4t },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/underwater-pipes-4ng4t | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:16:54+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "underwater-pipes-4ng4t", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "underwater-pipes", "1": "pipe"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:18:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for underwater-pipes-4ng4t
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
underwater-pipes-4ng4t
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for underwater-pipes-4ng4t\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nunderwater-pipes-4ng4t",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for underwater-pipes-4ng4t\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nunderwater-pipes-4ng4t",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for underwater-pipes-4ng4t\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nunderwater-pipes-4ng4t### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
b5f525d30ee0b55113e6a23ca75cd292f4fbd150 |
# Dataset Card for activity-diagrams-qdobr
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/activity-diagrams-qdobr
- **Point of Contact:** [email protected]
### Dataset Summary
activity-diagrams-qdobr
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/activity-diagrams-qdobr
### Citation Information
```
@misc{ activity-diagrams-qdobr,
title = { activity diagrams qdobr Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/activity-diagrams-qdobr } },
url = { https://universe.roboflow.com/object-detection/activity-diagrams-qdobr },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/activity-diagrams-qdobr | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:17:19+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "activity-diagrams-qdobr", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "activity-diagrams", "1": "action", "2": "activity", "3": "commeent", "4": "control_flow", "5": "control_flowcontrol_flow", "6": "decision_node", "7": "exit_node", "8": "final_flow_node", "9": "final_node", "10": "fork", "11": "merge", "12": "merge_noode", "14": "object", "15": "object_flow", "16": "signal_recept", "17": "signal_send", "18": "start_node", "19": "text"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:17:38+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for activity-diagrams-qdobr
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
activity-diagrams-qdobr
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for activity-diagrams-qdobr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nactivity-diagrams-qdobr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for activity-diagrams-qdobr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nactivity-diagrams-qdobr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for activity-diagrams-qdobr\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nactivity-diagrams-qdobr### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
0f63b9c16e63cef3ffc263b21793f93523d58877 |
# Dataset Card for tweeter-profile
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/tweeter-profile
- **Point of Contact:** [email protected]
### Dataset Summary
tweeter-profile
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/tweeter-profile
### Citation Information
```
@misc{ tweeter-profile,
title = { tweeter profile Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/tweeter-profile } },
url = { https://universe.roboflow.com/object-detection/tweeter-profile },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/tweeter-profile | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:17:39+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "tweeter-profile", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "tweeter-profile", "1": "profile_info"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:18:01+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for tweeter-profile
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
tweeter-profile
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for tweeter-profile\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntweeter-profile",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for tweeter-profile\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntweeter-profile",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
20,
233,
16,
5,
11,
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]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for tweeter-profile\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ntweeter-profile### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
63ecbe06216eb6e37bd6e714f917db3be3267414 |
# Dataset Card for circuit-voltages
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/circuit-voltages
- **Point of Contact:** [email protected]
### Dataset Summary
circuit-voltages
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/circuit-voltages
### Citation Information
```
@misc{ circuit-voltages,
title = { circuit voltages Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/circuit-voltages } },
url = { https://universe.roboflow.com/object-detection/circuit-voltages },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/circuit-voltages | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:18:01+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "circuit-voltages", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "circuit-voltages", "1": "GND", "2": "IDC", "3": "IDC_I", "4": "R", "5": "VDC", "6": "VDC_I"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:18:18+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for circuit-voltages
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
circuit-voltages
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for circuit-voltages\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncircuit-voltages",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for circuit-voltages\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncircuit-voltages",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
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17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for circuit-voltages\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncircuit-voltages### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
34188fb46437ad7f926c2c213ab8ee4aa776d658 |
# Dataset Card for hand-gestures-jps7z
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/hand-gestures-jps7z
- **Point of Contact:** [email protected]
### Dataset Summary
hand-gestures-jps7z
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/hand-gestures-jps7z
### Citation Information
```
@misc{ hand-gestures-jps7z,
title = { hand gestures jps7z Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/hand-gestures-jps7z } },
url = { https://universe.roboflow.com/object-detection/hand-gestures-jps7z },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/hand-gestures-jps7z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:18:16+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "hand-gestures-jps7z", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "hand-gestures", "1": 0, "2": 1, "3": 2, "4": 3, "5": 4, "6": 5, "7": 6, "8": 7, "9": 8, "10": 9, "11": 10, "12": 11, "13": 12, "14": 13}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:18:38+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for hand-gestures-jps7z
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
hand-gestures-jps7z
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for hand-gestures-jps7z\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nhand-gestures-jps7z",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for hand-gestures-jps7z\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nhand-gestures-jps7z",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
27,
22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for hand-gestures-jps7z\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nhand-gestures-jps7z### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
4abd6b78f94e949b53922c5cb7fd0934ff5500c6 |
# Dataset Card for paper-parts
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/paper-parts
- **Point of Contact:** [email protected]
### Dataset Summary
paper-parts
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/paper-parts
### Citation Information
```
@misc{ paper-parts,
title = { paper parts Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/paper-parts } },
url = { https://universe.roboflow.com/object-detection/paper-parts },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/paper-parts | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:18:19+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "paper-parts", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "paper-parts", "1": "author", "2": "chapter", "3": "equation", "4": "equation number", "5": "figure", "6": "figure caption", "7": "footnote", "8": "list of content heading", "9": "list of content text", "10": "page number", "11": "paragraph", "12": "reference text", "13": "section", "14": "subsection", "15": "subsubsection", "16": "table", "17": "table caption", "18": "table of contents text", "19": "title"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:20:46+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for paper-parts
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
paper-parts
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for paper-parts\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npaper-parts",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for paper-parts\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npaper-parts",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
11,
17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for paper-parts\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npaper-parts### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
e375556ab6cb6dfb8ea46030ee19625449c9fb13 |
# Dataset Card for bacteria-ptywi
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/bacteria-ptywi
- **Point of Contact:** [email protected]
### Dataset Summary
bacteria-ptywi
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bacteria-ptywi
### Citation Information
```
@misc{ bacteria-ptywi,
title = { bacteria ptywi Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bacteria-ptywi } },
url = { https://universe.roboflow.com/object-detection/bacteria-ptywi },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/bacteria-ptywi | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:18:38+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "bacteria-ptywi", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "bacteria", "1": "Str_pne"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:18:56+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for bacteria-ptywi
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
bacteria-ptywi
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for bacteria-ptywi\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbacteria-ptywi",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for bacteria-ptywi\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nbacteria-ptywi",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for bacteria-ptywi\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nbacteria-ptywi### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
86f8f00ceec516618a280eef24ff9bb2d9c7a2a5 |
# Dataset Card for thermal-dogs-and-people-x6ejw
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw
- **Point of Contact:** [email protected]
### Dataset Summary
thermal-dogs-and-people-x6ejw
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw
### Citation Information
```
@misc{ thermal-dogs-and-people-x6ejw,
title = { thermal dogs and people x6ejw Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw } },
url = { https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/thermal-dogs-and-people-x6ejw | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:18:56+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "thermal-dogs-and-people-x6ejw", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "thermal-dogs-n-people", "1": "dog", "2": "person"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:19:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for thermal-dogs-and-people-x6ejw
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
thermal-dogs-and-people-x6ejw
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for thermal-dogs-and-people-x6ejw\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nthermal-dogs-and-people-x6ejw",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for thermal-dogs-and-people-x6ejw\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nthermal-dogs-and-people-x6ejw",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
33,
22,
21,
33,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for thermal-dogs-and-people-x6ejw\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nthermal-dogs-and-people-x6ejw### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
93e8ccea7a18f0d0173be9d04da24fea9f607ead |
# Dataset Card for road-signs-6ih4y
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/road-signs-6ih4y
- **Point of Contact:** [email protected]
### Dataset Summary
road-signs-6ih4y
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/road-signs-6ih4y
### Citation Information
```
@misc{ road-signs-6ih4y,
title = { road signs 6ih4y Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/road-signs-6ih4y } },
url = { https://universe.roboflow.com/object-detection/road-signs-6ih4y },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/road-signs-6ih4y | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:19:15+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "road-signs-6ih4y", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "road-signs", "1": "bus_stop", "2": "do_not_enter", "3": "do_not_stop", "4": "do_not_turn_l", "5": "do_not_turn_r", "6": "do_not_u_turn", "7": "enter_left_lane", "8": "green_light", "9": "left_right_lane", "10": "no_parking", "11": "parking", "12": "ped_crossing", "13": "ped_zebra_cross", "14": "railway_crossing", "15": "red_light", "16": "stop", "17": "t_intersection_l", "18": "traffic_light", "19": "u_turn", "20": "warning", "21": "yellow_light"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:19:50+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for road-signs-6ih4y
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
road-signs-6ih4y
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for road-signs-6ih4y\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nroad-signs-6ih4y",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for road-signs-6ih4y\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nroad-signs-6ih4y",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for road-signs-6ih4y\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nroad-signs-6ih4y### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
8322549c0463710ad2f78ec10c2f712ff38dab6a |
# Dataset Card for cotton-20xz5
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cotton-20xz5
- **Point of Contact:** [email protected]
### Dataset Summary
cotton-20xz5
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cotton-20xz5
### Citation Information
```
@misc{ cotton-20xz5,
title = { cotton 20xz5 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cotton-20xz5 } },
url = { https://universe.roboflow.com/object-detection/cotton-20xz5 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cotton-20xz5 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:19:50+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cotton-20xz5", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cotton", "1": "G-arboreum", "2": "G-barbadense", "3": "G-herbaceum", "4": "G-hirsitum"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:20:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cotton-20xz5
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cotton-20xz5
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cotton-20xz5\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncotton-20xz5",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cotton-20xz5\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncotton-20xz5",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cotton-20xz5\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncotton-20xz5### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
41d65614ee3809b686b66bb57c1dc9ed2ce354aa |
# Dataset Card for cloud-types
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cloud-types
- **Point of Contact:** [email protected]
### Dataset Summary
cloud-types
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cloud-types
### Citation Information
```
@misc{ cloud-types,
title = { cloud types Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cloud-types } },
url = { https://universe.roboflow.com/object-detection/cloud-types },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cloud-types | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:23+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cloud-types", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cloud-types", "1": "Fish", "2": "Flower", "3": "Gravel", "4": "Sugar"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:56+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cloud-types
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cloud-types
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cloud-types\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncloud-types",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cloud-types\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncloud-types",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
11,
17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cloud-types\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncloud-types### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
b598e4ecc1e6492450ca86af9f85867d395ecd95 |
# Dataset Card for cable-damage
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cable-damage
- **Point of Contact:** [email protected]
### Dataset Summary
cable-damage
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cable-damage
### Citation Information
```
@misc{ cable-damage,
title = { cable damage Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cable-damage } },
url = { https://universe.roboflow.com/object-detection/cable-damage },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cable-damage | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:23+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cable-damage", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cable-damage", "1": "break", "2": "thunderbolt"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:29:47+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cable-damage
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cable-damage
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cable-damage\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncable-damage",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cable-damage\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncable-damage",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cable-damage\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncable-damage### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a6c08f77c87f0f518a5768f41d9949ef96da5bf8 |
# Dataset Card for sign-language-sokdr
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/sign-language-sokdr
- **Point of Contact:** [email protected]
### Dataset Summary
sign-language-sokdr
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/sign-language-sokdr
### Citation Information
```
@misc{ sign-language-sokdr,
title = { sign language sokdr Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/sign-language-sokdr } },
url = { https://universe.roboflow.com/object-detection/sign-language-sokdr },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/sign-language-sokdr | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:23+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "sign-language-sokdr", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "sign-language", "1": "A", "2": "B", "3": "C", "4": "D", "5": "E", "6": "F", "7": "G", "8": "H", "9": "I", "10": "J", "11": "K", "12": "L", "13": "M", "14": "N", "15": "O", "16": "P", "17": "Q", "18": "R", "19": "S", "20": "T", "21": "U", "22": "V", "23": "W", "24": "X", "25": "Y", "26": "Z"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:29:42+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for sign-language-sokdr
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
sign-language-sokdr
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for sign-language-sokdr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsign-language-sokdr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for sign-language-sokdr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsign-language-sokdr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
16,
5,
11,
17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for sign-language-sokdr\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsign-language-sokdr### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
14f318be607371ffa7c3366e09e6298af1bc683c |
# Dataset Card for weed-crop-aerial
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/weed-crop-aerial
- **Point of Contact:** [email protected]
### Dataset Summary
weed-crop-aerial
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/weed-crop-aerial
### Citation Information
```
@misc{ weed-crop-aerial,
title = { weed crop aerial Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/weed-crop-aerial } },
url = { https://universe.roboflow.com/object-detection/weed-crop-aerial },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/weed-crop-aerial | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:23+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "weed-crop-aerial", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "weed-crop-aerial", "1": "crop", "2": "weed"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:29:52+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for weed-crop-aerial
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
weed-crop-aerial
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for weed-crop-aerial\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nweed-crop-aerial",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for weed-crop-aerial\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nweed-crop-aerial",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
27,
22,
15,
33,
5,
6,
20,
233,
16,
5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for weed-crop-aerial\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nweed-crop-aerial### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
d807d10f8a8ce4aa40ad662924e5d5c52239c51e |
# Dataset Card for wall-damage
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/wall-damage
- **Point of Contact:** [email protected]
### Dataset Summary
wall-damage
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/wall-damage
### Citation Information
```
@misc{ wall-damage,
title = { wall damage Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/wall-damage } },
url = { https://universe.roboflow.com/object-detection/wall-damage },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/wall-damage | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:43+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "wall-damage", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "wall-damage", "1": "Minorrotation", "2": "Moderaterotation", "3": "Severerotation"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:29:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for wall-damage
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
wall-damage
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for wall-damage\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nwall-damage",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for wall-damage\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nwall-damage",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for wall-damage\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nwall-damage### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
b1490a11c0bed13ed720730e3666625400f31c85 |
# Dataset Card for animals-ij5d2
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/animals-ij5d2
- **Point of Contact:** [email protected]
### Dataset Summary
animals-ij5d2
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/animals-ij5d2
### Citation Information
```
@misc{ animals-ij5d2,
title = { animals ij5d2 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/animals-ij5d2 } },
url = { https://universe.roboflow.com/object-detection/animals-ij5d2 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/animals-ij5d2 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:48+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "animals-ij5d2", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "animals", "1": "cat", "2": "chicken", "3": "cow", "4": "dog", "5": "fox", "6": "goat", "7": "horse", "8": "person", "9": "racoon", "10": "skunk"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:09+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for animals-ij5d2
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
animals-ij5d2
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for animals-ij5d2\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nanimals-ij5d2",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for animals-ij5d2\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nanimals-ij5d2",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for animals-ij5d2\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nanimals-ij5d2### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
e286c1630cff62a248c1507016b84db4324a5fa5 |
# Dataset Card for uno-deck
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/uno-deck
- **Point of Contact:** [email protected]
### Dataset Summary
uno-deck
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/uno-deck
### Citation Information
```
@misc{ uno-deck,
title = { uno deck Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/uno-deck } },
url = { https://universe.roboflow.com/object-detection/uno-deck },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/uno-deck | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:53+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "uno-deck", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "uno-deck", "1": 0, "2": 1, "3": 2, "4": 3, "5": 4, "6": 5, "7": 6, "8": 7, "9": 8, "10": 9, "11": 10, "12": 11, "13": 12, "14": 13, "15": 14}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:53+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for uno-deck
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
uno-deck
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for uno-deck\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nuno-deck",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for uno-deck\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nuno-deck",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
21,
22,
9,
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6,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for uno-deck\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nuno-deck### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
9801096c1b38433131b3d425b67b2a75945deaf9 |
# Dataset Card for avatar-recognition-nuexe
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/avatar-recognition-nuexe
- **Point of Contact:** [email protected]
### Dataset Summary
avatar-recognition-nuexe
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/avatar-recognition-nuexe
### Citation Information
```
@misc{ avatar-recognition-nuexe,
title = { avatar recognition nuexe Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/avatar-recognition-nuexe } },
url = { https://universe.roboflow.com/object-detection/avatar-recognition-nuexe },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/avatar-recognition-nuexe | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:29:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "avatar-recognition-nuexe", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "avatar", "1": "Character"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:13+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for avatar-recognition-nuexe
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
avatar-recognition-nuexe
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for avatar-recognition-nuexe\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\navatar-recognition-nuexe",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for avatar-recognition-nuexe\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\navatar-recognition-nuexe",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
26,
22,
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33,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for avatar-recognition-nuexe\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\navatar-recognition-nuexe### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a6cdb168089363c701a537c9b94c8358f609df98 |
# Dataset Card for cotton-plant-disease
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cotton-plant-disease
- **Point of Contact:** [email protected]
### Dataset Summary
cotton-plant-disease
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cotton-plant-disease
### Citation Information
```
@misc{ cotton-plant-disease,
title = { cotton plant disease Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cotton-plant-disease } },
url = { https://universe.roboflow.com/object-detection/cotton-plant-disease },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cotton-plant-disease | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:10+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cotton-plant-disease", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cotton-plant-disease", "1": "dc"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cotton-plant-disease
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cotton-plant-disease
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cotton-plant-disease\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncotton-plant-disease",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cotton-plant-disease\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncotton-plant-disease",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cotton-plant-disease\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncotton-plant-disease### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
51765b0d63b11ab6c980e1574a5dc9010f34b280 |
# Dataset Card for x-ray-rheumatology
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/x-ray-rheumatology
- **Point of Contact:** [email protected]
### Dataset Summary
x-ray-rheumatology
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/x-ray-rheumatology
### Citation Information
```
@misc{ x-ray-rheumatology,
title = { x ray rheumatology Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/x-ray-rheumatology } },
url = { https://universe.roboflow.com/object-detection/x-ray-rheumatology },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/x-ray-rheumatology | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:14+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "x-ray-rheumatology", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "x-ray-rheumatology", "1": "artefact", "2": "distal phalanges", "3": "fifth metacarpal bone", "4": "first metacarpal bone", "5": "fourth metacarpal bone", "6": "intermediate phalanges", "7": "proximal phalanges", "8": "radius", "9": "second metacarpal bone", "10": "soft tissue calcination", "11": "third metacarpal bone", "12": "ulna"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:27+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for x-ray-rheumatology
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
x-ray-rheumatology
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for x-ray-rheumatology\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nx-ray-rheumatology",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for x-ray-rheumatology\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nx-ray-rheumatology",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for x-ray-rheumatology\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nx-ray-rheumatology### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
59b6b8327e6b93e544d26f988f86cda0bf896336 |
# Dataset Card for cavity-rs0uf
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cavity-rs0uf
- **Point of Contact:** [email protected]
### Dataset Summary
cavity-rs0uf
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cavity-rs0uf
### Citation Information
```
@misc{ cavity-rs0uf,
title = { cavity rs0uf Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cavity-rs0uf } },
url = { https://universe.roboflow.com/object-detection/cavity-rs0uf },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cavity-rs0uf | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:28+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cavity-rs0uf", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cavity-0", "1": "cavity", "2": "normal"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:44+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cavity-rs0uf
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cavity-rs0uf
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cavity-rs0uf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncavity-rs0uf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cavity-rs0uf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncavity-rs0uf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cavity-rs0uf\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncavity-rs0uf### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
1d82b015cea3a7ff9859167e1d0f8971291bf0b4 |
# Dataset Card for peanuts-sd4kf
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/peanuts-sd4kf
- **Point of Contact:** [email protected]
### Dataset Summary
peanuts-sd4kf
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/peanuts-sd4kf
### Citation Information
```
@misc{ peanuts-sd4kf,
title = { peanuts sd4kf Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/peanuts-sd4kf } },
url = { https://universe.roboflow.com/object-detection/peanuts-sd4kf },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/peanuts-sd4kf | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:40+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "peanuts-sd4kf", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "peanuts", "1": "with mold", "2": "without mold"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for peanuts-sd4kf
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
peanuts-sd4kf
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for peanuts-sd4kf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npeanuts-sd4kf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for peanuts-sd4kf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npeanuts-sd4kf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for peanuts-sd4kf\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npeanuts-sd4kf### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
7e399dd1508105f9885f6fe3c7789cd605b9fa43 |
# Dataset Card for marbles
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/marbles
- **Point of Contact:** [email protected]
### Dataset Summary
marbles
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/marbles
### Citation Information
```
@misc{ marbles,
title = { marbles Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/marbles } },
url = { https://universe.roboflow.com/object-detection/marbles },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/marbles | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:45+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "marbles", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "marbles", "1": "red", "2": "white"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:30:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for marbles
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
marbles
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for marbles\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nmarbles",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for marbles\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nmarbles",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for marbles\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nmarbles### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
99b4a366289e1314fa9a3571e18cb389dd2f5efc |
# Dataset Card for apples-fvpl5
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/apples-fvpl5
- **Point of Contact:** [email protected]
### Dataset Summary
apples-fvpl5
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/apples-fvpl5
### Citation Information
```
@misc{ apples-fvpl5,
title = { apples fvpl5 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/apples-fvpl5 } },
url = { https://universe.roboflow.com/object-detection/apples-fvpl5 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/apples-fvpl5 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:57+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "apples-fvpl5", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "apples", "1": "apple", "2": "damaged_apple"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for apples-fvpl5
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
apples-fvpl5
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for apples-fvpl5\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\napples-fvpl5",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for apples-fvpl5\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\napples-fvpl5",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
13,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for apples-fvpl5\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\napples-fvpl5### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
6f511210ac718b282a0c7506567b61cfc2720d2c |
# Dataset Card for leaf-disease-nsdsr
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/leaf-disease-nsdsr
- **Point of Contact:** [email protected]
### Dataset Summary
leaf-disease-nsdsr
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/leaf-disease-nsdsr
### Citation Information
```
@misc{ leaf-disease-nsdsr,
title = { leaf disease nsdsr Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/leaf-disease-nsdsr } },
url = { https://universe.roboflow.com/object-detection/leaf-disease-nsdsr },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/leaf-disease-nsdsr | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "leaf-disease-nsdsr", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "leaf-disease", "1": "mildew", "2": "rose_P01", "3": "rose_R02"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:29+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for leaf-disease-nsdsr
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
leaf-disease-nsdsr
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for leaf-disease-nsdsr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nleaf-disease-nsdsr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for leaf-disease-nsdsr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nleaf-disease-nsdsr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for leaf-disease-nsdsr\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nleaf-disease-nsdsr### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
92460814e77f2874b3514da063b6caa39e8a9abb |
# Dataset Card for document-parts
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/document-parts
- **Point of Contact:** [email protected]
### Dataset Summary
document-parts
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/document-parts
### Citation Information
```
@misc{ document-parts,
title = { document parts Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/document-parts } },
url = { https://universe.roboflow.com/object-detection/document-parts },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/document-parts | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:30:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "document-parts", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "document-parts", "1": "table", "2": "title"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:28+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for document-parts
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
document-parts
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for document-parts\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ndocument-parts",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for document-parts\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ndocument-parts",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for document-parts\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ndocument-parts### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
b5861bc164a75f265e2b2711c66525612a472cdc |
# Dataset Card for gynecology-mri
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/gynecology-mri
- **Point of Contact:** [email protected]
### Dataset Summary
gynecology-mri
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/gynecology-mri
### Citation Information
```
@misc{ gynecology-mri,
title = { gynecology mri Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/gynecology-mri } },
url = { https://universe.roboflow.com/object-detection/gynecology-mri },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/gynecology-mri | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:16+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "gynecology-mri", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "gynecology-MRI", "1": "6W", "2": "7W", "3": "EH"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:43+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for gynecology-mri
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
gynecology-mri
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for gynecology-mri\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ngynecology-mri",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for gynecology-mri\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ngynecology-mri",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for gynecology-mri\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ngynecology-mri### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
5eeeb6796fa3cf668e7c1eda2ee19845c21d8d2c |
# Dataset Card for mask-wearing-608pr
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/mask-wearing-608pr
- **Point of Contact:** [email protected]
### Dataset Summary
mask-wearing-608pr
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/mask-wearing-608pr
### Citation Information
```
@misc{ mask-wearing-608pr,
title = { mask wearing 608pr Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/mask-wearing-608pr } },
url = { https://universe.roboflow.com/object-detection/mask-wearing-608pr },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/mask-wearing-608pr | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:28+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "mask-wearing-608pr", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "mask-wearing", "1": "mask", "2": "no-mask"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:42+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for mask-wearing-608pr
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
mask-wearing-608pr
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for mask-wearing-608pr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nmask-wearing-608pr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for mask-wearing-608pr\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nmask-wearing-608pr",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for mask-wearing-608pr\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nmask-wearing-608pr### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
abc5420a7d6b12fa426f5a2b6fb114f92f01cbda |
# Dataset Card for coral-lwptl
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/coral-lwptl
- **Point of Contact:** [email protected]
### Dataset Summary
coral-lwptl
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/coral-lwptl
### Citation Information
```
@misc{ coral-lwptl,
title = { coral lwptl Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/coral-lwptl } },
url = { https://universe.roboflow.com/object-detection/coral-lwptl },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/coral-lwptl | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:30+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "coral-lwptl", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "coral", "1": "Arborescent", "2": "Caespitose-a", "3": "Caespitose-b", "4": "Columnar", "5": "Corymbose", "6": "Digitate", "7": "Encrusting", "8": "Foliose", "9": "Massive-Faviidae", "10": "Massive-Merulinidae", "11": "Massive-Mussidae", "12": "Massive-Poritidae", "13": "Solitary", "14": "Tabular"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:51+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for coral-lwptl
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
coral-lwptl
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for coral-lwptl\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncoral-lwptl",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for coral-lwptl\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncoral-lwptl",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for coral-lwptl\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncoral-lwptl### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
7635d0594066f3b6372197a986f256c25373498e |
# Dataset Card for sedimentary-features-9eosf
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/sedimentary-features-9eosf
- **Point of Contact:** [email protected]
### Dataset Summary
sedimentary-features-9eosf
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/sedimentary-features-9eosf
### Citation Information
```
@misc{ sedimentary-features-9eosf,
title = { sedimentary features 9eosf Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/sedimentary-features-9eosf } },
url = { https://universe.roboflow.com/object-detection/sedimentary-features-9eosf },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/sedimentary-features-9eosf | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:43+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "sedimentary-features-9eosf", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "sediment", "1": "Cross bedding", "2": "Low angle", "3": "Massive", "4": "Parallel lamination", "5": "mud drape"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for sedimentary-features-9eosf
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
sedimentary-features-9eosf
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for sedimentary-features-9eosf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsedimentary-features-9eosf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for sedimentary-features-9eosf\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsedimentary-features-9eosf",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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78,
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for sedimentary-features-9eosf\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsedimentary-features-9eosf### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
f9dfc9f71ce1151ab14f059eb4ed337a8bc993fb |
# Dataset Card for chess-pieces-mjzgj
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/chess-pieces-mjzgj
- **Point of Contact:** [email protected]
### Dataset Summary
chess-pieces-mjzgj
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/chess-pieces-mjzgj
### Citation Information
```
@misc{ chess-pieces-mjzgj,
title = { chess pieces mjzgj Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/chess-pieces-mjzgj } },
url = { https://universe.roboflow.com/object-detection/chess-pieces-mjzgj },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/chess-pieces-mjzgj | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:44+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "chess-pieces-mjzgj", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "chess-pieces", "1": "bishop", "2": "black-bishop", "3": "black-king", "4": "black-knight", "5": "black-pawn", "6": "black-queen", "7": "black-rook", "8": "white-bishop", "9": "white-king", "10": "white-knight", "11": "white-pawn", "12": "white-queen", "13": "white-rook"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:31:59+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for chess-pieces-mjzgj
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
chess-pieces-mjzgj
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for chess-pieces-mjzgj\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nchess-pieces-mjzgj",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for chess-pieces-mjzgj\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nchess-pieces-mjzgj",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
29,
22,
17,
33,
5,
6,
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233,
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5,
11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for chess-pieces-mjzgj\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nchess-pieces-mjzgj### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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]
|
a523fc386ec3121909f1994b5259515ee2e1913a |
# Dataset Card for robomasters-285km
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/robomasters-285km
- **Point of Contact:** [email protected]
### Dataset Summary
robomasters-285km
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/robomasters-285km
### Citation Information
```
@misc{ robomasters-285km,
title = { robomasters 285km Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/robomasters-285km } },
url = { https://universe.roboflow.com/object-detection/robomasters-285km },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/robomasters-285km | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:52+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "robomasters-285km", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "robots", "1": "armor", "2": "base", "3": "car", "4": "rune", "5": "rune-blue", "6": "rune-gray", "7": "rune-grey", "8": "rune-red", "9": "watcher"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:32:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for robomasters-285km
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
robomasters-285km
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for robomasters-285km\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nrobomasters-285km",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for robomasters-285km\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nrobomasters-285km",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for robomasters-285km\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nrobomasters-285km### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
f9c047d91513beccd5072c5e8f2e48340693a576 |
# Dataset Card for number-ops
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/number-ops
- **Point of Contact:** [email protected]
### Dataset Summary
number-ops
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/number-ops
### Citation Information
```
@misc{ number-ops,
title = { number ops Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/number-ops } },
url = { https://universe.roboflow.com/object-detection/number-ops },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/number-ops | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:54+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "number-ops", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "number-ops", "1": 0, "2": 1, "3": 2, "4": 3, "5": 4, "6": 5, "7": 6, "8": 7, "9": 8, "10": 9, "11": "div", "12": "eqv", "13": "minus", "14": "mult", "15": "plus"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:32:20+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for number-ops
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
number-ops
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for number-ops\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nnumber-ops",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for number-ops\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nnumber-ops",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for number-ops\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nnumber-ops### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
5d98f8ebc2437e51f3e1adcadc6a725c9cb02699 |
# Dataset Card for stomata-cells
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/stomata-cells
- **Point of Contact:** [email protected]
### Dataset Summary
stomata-cells
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/stomata-cells
### Citation Information
```
@misc{ stomata-cells,
title = { stomata cells Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/stomata-cells } },
url = { https://universe.roboflow.com/object-detection/stomata-cells },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/stomata-cells | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:31:59+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "stomata-cells", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "stomata-cells", "1": "close", "2": "open"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:32:34+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for stomata-cells
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
stomata-cells
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for stomata-cells\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nstomata-cells",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for stomata-cells\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nstomata-cells",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
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233,
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11,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for stomata-cells\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nstomata-cells### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
1f4416ad8ff21fec12ae8742d9c79d43781c7fae |
# Dataset Card for mitosis-gjs3g
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/mitosis-gjs3g
- **Point of Contact:** [email protected]
### Dataset Summary
mitosis-gjs3g
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/mitosis-gjs3g
### Citation Information
```
@misc{ mitosis-gjs3g,
title = { mitosis gjs3g Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/mitosis-gjs3g } },
url = { https://universe.roboflow.com/object-detection/mitosis-gjs3g },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/mitosis-gjs3g | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:32:00+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "mitosis-gjs3g", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "mitosis", "1": "Mitosis"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:32:18+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for mitosis-gjs3g
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
mitosis-gjs3g
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for mitosis-gjs3g\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nmitosis-gjs3g",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for mitosis-gjs3g\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nmitosis-gjs3g",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for mitosis-gjs3g\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nmitosis-gjs3g### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
0e304ea035f5ac7f3c724d073534668274d680d7 |
# Dataset Card for smoke-uvylj
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/smoke-uvylj
- **Point of Contact:** [email protected]
### Dataset Summary
smoke-uvylj
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/smoke-uvylj
### Citation Information
```
@misc{ smoke-uvylj,
title = { smoke uvylj Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/smoke-uvylj } },
url = { https://universe.roboflow.com/object-detection/smoke-uvylj },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/smoke-uvylj | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:32:19+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "smoke-uvylj", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "smoke-0", "1": "smoke"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:32:38+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for smoke-uvylj
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
smoke-uvylj
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for smoke-uvylj\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsmoke-uvylj",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for smoke-uvylj\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsmoke-uvylj",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for smoke-uvylj\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsmoke-uvylj### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
c6611cc56c054fee1083089edb71614866ac8f96 |
# Dataset Card for aerial-spheres
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/aerial-spheres
- **Point of Contact:** [email protected]
### Dataset Summary
aerial-spheres
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/aerial-spheres
### Citation Information
```
@misc{ aerial-spheres,
title = { aerial spheres Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/aerial-spheres } },
url = { https://universe.roboflow.com/object-detection/aerial-spheres },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/aerial-spheres | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:32:21+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "aerial-spheres", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "aerial-spheres", "1": "green_sphero", "2": "orange-sphero", "3": "orange_sphero", "4": "purple_sphero", "5": "red_sphero", "6": "yellow_sphero"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:32:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for aerial-spheres
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
aerial-spheres
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for aerial-spheres\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naerial-spheres",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for aerial-spheres\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naerial-spheres",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
12,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for aerial-spheres\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\naerial-spheres### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
1b7367b5b3e359d23ace87d8f89053e7e6ee43fa |
# Dataset Card for excavators-czvg9
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/excavators-czvg9
- **Point of Contact:** [email protected]
### Dataset Summary
excavators-czvg9
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/excavators-czvg9
### Citation Information
```
@misc{ excavators-czvg9,
title = { excavators czvg9 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/excavators-czvg9 } },
url = { https://universe.roboflow.com/object-detection/excavators-czvg9 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/excavators-czvg9 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:32:37+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "excavators-czvg9", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "excavators", "1": "EXCAVATORS", "2": "dump truck", "3": "wheel loader"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:33:23+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for excavators-czvg9
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
excavators-czvg9
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for excavators-czvg9\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nexcavators-czvg9",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for excavators-czvg9\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nexcavators-czvg9",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for excavators-czvg9\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nexcavators-czvg9### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
1b6e0d5603507d44663b66c61c581220bc3d8d60 |
# Dataset Card for signatures-xc8up
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/signatures-xc8up
- **Point of Contact:** [email protected]
### Dataset Summary
signatures-xc8up
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/signatures-xc8up
### Citation Information
```
@misc{ signatures-xc8up,
title = { signatures xc8up Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/signatures-xc8up } },
url = { https://universe.roboflow.com/object-detection/signatures-xc8up },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/signatures-xc8up | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:33:06+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "signatures-xc8up", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "signatures", "1": "signature"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:33:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for signatures-xc8up
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
signatures-xc8up
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for signatures-xc8up\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsignatures-xc8up",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for signatures-xc8up\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nsignatures-xc8up",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for signatures-xc8up\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nsignatures-xc8up### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a23cb6c1a333daf110532c3fad58ed564b14ae4b |
# Dataset Card for underwater-objects-5v7p8
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/underwater-objects-5v7p8
- **Point of Contact:** [email protected]
### Dataset Summary
underwater-objects-5v7p8
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/underwater-objects-5v7p8
### Citation Information
```
@misc{ underwater-objects-5v7p8,
title = { underwater objects 5v7p8 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/underwater-objects-5v7p8 } },
url = { https://universe.roboflow.com/object-detection/underwater-objects-5v7p8 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/underwater-objects-5v7p8 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:36:52+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "underwater-objects-5v7p8", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "underwater-objects", "1": "echinus", "2": "holothurian", "3": "scallop", "4": "starfish", "5": "waterweeds"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:38:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for underwater-objects-5v7p8
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
underwater-objects-5v7p8
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for underwater-objects-5v7p8\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nunderwater-objects-5v7p8",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for underwater-objects-5v7p8\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nunderwater-objects-5v7p8",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
28,
22,
16,
33,
5,
6,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for underwater-objects-5v7p8\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nunderwater-objects-5v7p8### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
298ab316ba94d7eb381bb3403ed5119fa6067d0b |
# Dataset Card for people-in-paintings
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/people-in-paintings
- **Point of Contact:** [email protected]
### Dataset Summary
people-in-paintings
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/people-in-paintings
### Citation Information
```
@misc{ people-in-paintings,
title = { people in paintings Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/people-in-paintings } },
url = { https://universe.roboflow.com/object-detection/people-in-paintings },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/people-in-paintings | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:36:52+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "people-in-paintings", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "people-in-paintings", "1": "Human"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:37:23+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for people-in-paintings
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
people-in-paintings
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for people-in-paintings\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npeople-in-paintings",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for people-in-paintings\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npeople-in-paintings",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for people-in-paintings\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npeople-in-paintings### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
96171862061a06f46e26625356d4b09633000c1b |
# Dataset Card for washroom-rf1fa
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/washroom-rf1fa
- **Point of Contact:** [email protected]
### Dataset Summary
washroom-rf1fa
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/washroom-rf1fa
### Citation Information
```
@misc{ washroom-rf1fa,
title = { washroom rf1fa Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/washroom-rf1fa } },
url = { https://universe.roboflow.com/object-detection/washroom-rf1fa },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/washroom-rf1fa | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:36:53+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "washroom-rf1fa", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "washroom", "1": "bathtub", "2": "c", "3": "geyser", "4": "mirror", "5": "showerhead", "6": "sink", "7": "toilet", "8": "towel", "9": "washbasin", "10": "wc"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:37:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for washroom-rf1fa
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
washroom-rf1fa
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for washroom-rf1fa\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nwashroom-rf1fa",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for washroom-rf1fa\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nwashroom-rf1fa",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
13,
33,
5,
6,
20,
233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for washroom-rf1fa\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nwashroom-rf1fa### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
5f30c22dc73827bf76ad87d140c0dcee6e633030 |
# Dataset Card for farcry6-videogame
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/farcry6-videogame
- **Point of Contact:** [email protected]
### Dataset Summary
farcry6-videogame
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/farcry6-videogame
### Citation Information
```
@misc{ farcry6-videogame,
title = { farcry6 videogame Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/farcry6-videogame } },
url = { https://universe.roboflow.com/object-detection/farcry6-videogame },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/farcry6-videogame | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:37:23+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "farcry6-videogame", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "farcry6", "1": "assassin", "2": "atv", "3": "car", "4": "gun", "5": "gun menu", "6": "healthbar", "7": "horse", "8": "hud", "9": "map", "10": "person", "11": "surroundings"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:37:41+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for farcry6-videogame
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
farcry6-videogame
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for farcry6-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nfarcry6-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for farcry6-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nfarcry6-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
13,
33,
5,
6,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for farcry6-videogame\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nfarcry6-videogame### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
a3bdbe6542b77b62f9bdce0532e912b62ed15f27 |
# Dataset Card for grass-weeds
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/grass-weeds
- **Point of Contact:** [email protected]
### Dataset Summary
grass-weeds
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/grass-weeds
### Citation Information
```
@misc{ grass-weeds,
title = { grass weeds Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/grass-weeds } },
url = { https://universe.roboflow.com/object-detection/grass-weeds },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/grass-weeds | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:37:38+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "grass-weeds", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "grass-weeds", "1": "0 ridderzuring"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:39:10+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for grass-weeds
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
grass-weeds
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for grass-weeds\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ngrass-weeds",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for grass-weeds\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ngrass-weeds",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
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33,
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6,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for grass-weeds\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ngrass-weeds### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
d71741576660c41403e43972e07796e55ca292eb |
# Dataset Card for wine-labels
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/wine-labels
- **Point of Contact:** [email protected]
### Dataset Summary
wine-labels
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/wine-labels
### Citation Information
```
@misc{ wine-labels,
title = { wine labels Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/wine-labels } },
url = { https://universe.roboflow.com/object-detection/wine-labels },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/wine-labels | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:37:41+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "wine-labels", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "wine-labels", "1": "AlcoholPercentage", "2": "Appellation AOC DOC AVARegion", "3": "Appellation QualityLevel", "4": "CountryCountry", "5": "Distinct Logo", "6": "Established YearYear", "7": "Maker-Name", "8": "Organic", "9": "Sustainable", "10": "Sweetness-Brut-SecSweetness-Brut-Sec", "11": "TypeWine Type", "12": "VintageYear"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:38:32+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for wine-labels
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
wine-labels
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for wine-labels\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nwine-labels",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for wine-labels\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nwine-labels",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for wine-labels\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nwine-labels### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
964a5e5047b50596292e7c1bb47dd9f0f9293aa7 |
# Dataset Card for pests-2xlvx
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/pests-2xlvx
- **Point of Contact:** [email protected]
### Dataset Summary
pests-2xlvx
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/pests-2xlvx
### Citation Information
```
@misc{ pests-2xlvx,
title = { pests 2xlvx Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/pests-2xlvx } },
url = { https://universe.roboflow.com/object-detection/pests-2xlvx },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/pests-2xlvx | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:38:33+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "pests-2xlvx", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "pests", "1": "Agrotis", "2": "Athetis lepigone", "3": "Athetis lineosa", "4": "Chilo suppressalis", "5": "Cnaphalocrocis medinalis Guenee", "6": "Creatonotus transiens", "7": "Diaphania indica", "8": "Endotricha consocia", "9": "Euproctis sparsa", "10": "Gryllidae", "11": "Gryllotalpidae", "12": "Helicoverpa armigera", "13": "Holotrichia oblita Faldermann", "14": "Loxostege sticticalis", "15": "Mamestra brassicae", "16": "Maruca testulalis Geyer", "17": "Mythimna separata", "18": "Naranga aenescens Moore", "19": "Nilaparvata", "20": "Paracymoriza taiwanalis", "21": "Sesamia inferens", "22": "Sirthenea flavipes", "23": "Sogatella furcifera", "24": "Spodoptera exigua", "25": "Spoladea recurvalis", "26": "Staurophora celsia", "27": "Timandra Recompta", "28": "Trichoptera"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:39:02+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for pests-2xlvx
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
pests-2xlvx
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for pests-2xlvx\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npests-2xlvx",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for pests-2xlvx\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\npests-2xlvx",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
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"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for pests-2xlvx\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\npests-2xlvx### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
bbacc478b69d401033b5327100ab5112702644fd |
# Dataset Card for currency-v4f8j
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/currency-v4f8j
- **Point of Contact:** [email protected]
### Dataset Summary
currency-v4f8j
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/currency-v4f8j
### Citation Information
```
@misc{ currency-v4f8j,
title = { currency v4f8j Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/currency-v4f8j } },
url = { https://universe.roboflow.com/object-detection/currency-v4f8j },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/currency-v4f8j | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:38:40+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "currency-v4f8j", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "currency", "1": "Dime", "2": "Nickel", "3": "Penny", "4": "Quarter", "5": "fifty", "6": "five", "7": "hundred", "8": "one", "9": "ten", "10": "twenty"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:39:07+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for currency-v4f8j
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
currency-v4f8j
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for currency-v4f8j\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncurrency-v4f8j",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for currency-v4f8j\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncurrency-v4f8j",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
25,
22,
13,
33,
5,
6,
20,
233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for currency-v4f8j\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncurrency-v4f8j### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
f5405402f50eda7be4ceacd2169b0047287cb182 |
# Dataset Card for cables-nl42k
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cables-nl42k
- **Point of Contact:** [email protected]
### Dataset Summary
cables-nl42k
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cables-nl42k
### Citation Information
```
@misc{ cables-nl42k,
title = { cables nl42k Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cables-nl42k } },
url = { https://universe.roboflow.com/object-detection/cables-nl42k },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cables-nl42k | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:39:07+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cables-nl42k", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "cables", "1": "Antenne", "2": "BBS", "3": "BFU", "4": "Batterie", "5": "DDF", "6": "PCF", "7": "PCU AC", "8": "PCU DC", "9": "PDU", "10": "PSU", "11": "RBS"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:40:35+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cables-nl42k
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cables-nl42k
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cables-nl42k\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncables-nl42k",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cables-nl42k\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncables-nl42k",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cables-nl42k\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncables-nl42k### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
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|
0b502f037865f4cf9ec1254a8c34fc3f19caaeef |
# Dataset Card for axial-mri
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/axial-mri
- **Point of Contact:** [email protected]
### Dataset Summary
axial-mri
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/axial-mri
### Citation Information
```
@misc{ axial-mri,
title = { axial mri Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/axial-mri } },
url = { https://universe.roboflow.com/object-detection/axial-mri },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/axial-mri | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:39:10+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "axial-mri", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "axial-MRI", "1": "negative", "2": "positive"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:39:28+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for axial-mri
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
axial-mri
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for axial-mri\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naxial-mri",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for axial-mri\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\naxial-mri",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for axial-mri\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\naxial-mri### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
33dc733ce0bb0ca40c1b0f521b204036eb4311d0 |
# Dataset Card for 4-fold-defect
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/4-fold-defect
- **Point of Contact:** [email protected]
### Dataset Summary
4-fold-defect
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/4-fold-defect
### Citation Information
```
@misc{ 4-fold-defect,
title = { 4 fold defect Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/4-fold-defect } },
url = { https://universe.roboflow.com/object-detection/4-fold-defect },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/4-fold-defect | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:40:36+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "4-fold-defect", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "4-fold-defect", "1": "4-fold defect"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:41:00+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for 4-fold-defect
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
4-fold-defect
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for 4-fold-defect\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\n4-fold-defect",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for 4-fold-defect\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\n4-fold-defect",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
20,
233,
16,
5,
11,
17
]
| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for 4-fold-defect\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\n4-fold-defect### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
452f44c3b625f98ce35435d75fac3aede3d90040 |
# Dataset Card for tweeter-posts
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/tweeter-posts
- **Point of Contact:** [email protected]
### Dataset Summary
tweeter-posts
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/tweeter-posts
### Citation Information
```
@misc{ tweeter-posts,
title = { tweeter posts Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/tweeter-posts } },
url = { https://universe.roboflow.com/object-detection/tweeter-posts },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/tweeter-posts | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:41:01+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "tweeter-posts", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "tweeter-posts", "1": "caption", "2": "tweet"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:41:19+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for tweeter-posts
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
tweeter-posts
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for tweeter-posts\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntweeter-posts",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for tweeter-posts\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ntweeter-posts",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
23,
22,
11,
33,
5,
6,
20,
233,
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5,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for tweeter-posts\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ntweeter-posts### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
f3d6cf31861d2d669fd43c7a872b928c190ce865 |
# Dataset Card for abdomen-mri
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/abdomen-mri
- **Point of Contact:** [email protected]
### Dataset Summary
abdomen-mri
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/abdomen-mri
### Citation Information
```
@misc{ abdomen-mri,
title = { abdomen mri Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/abdomen-mri } },
url = { https://universe.roboflow.com/object-detection/abdomen-mri },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/abdomen-mri | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T08:41:19+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "abdomen-mri", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "abdomen-MRI", "1": 0}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T08:41:54+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for abdomen-mri
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
abdomen-mri
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for abdomen-mri\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nabdomen-mri",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for abdomen-mri\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nabdomen-mri",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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22,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for abdomen-mri\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nabdomen-mri### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
48663037c6ee409342cb8573eb736d3bdc453e85 |
# Dataset Card for cell-towers
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/cell-towers
- **Point of Contact:** [email protected]
### Dataset Summary
cell-towers
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/cell-towers
### Citation Information
```
@misc{ cell-towers,
title = { cell towers Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/cell-towers } },
url = { https://universe.roboflow.com/object-detection/cell-towers },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/cell-towers | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T09:02:35+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "cell-towers", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "pieces", "1": "joint", "2": "side"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T09:02:54+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for cell-towers
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
cell-towers
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for cell-towers\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncell-towers",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for cell-towers\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncell-towers",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
22,
22,
10,
33,
5,
6,
20,
233,
16,
5,
11,
17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for cell-towers\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncell-towers### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
364667ff14910673c4c23db5a63af284d0d8e803 |
# Dataset Card for corrosion-bi3q3
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/corrosion-bi3q3
- **Point of Contact:** [email protected]
### Dataset Summary
corrosion-bi3q3
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/corrosion-bi3q3
### Citation Information
```
@misc{ corrosion-bi3q3,
title = { corrosion bi3q3 Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/corrosion-bi3q3 } },
url = { https://universe.roboflow.com/object-detection/corrosion-bi3q3 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/corrosion-bi3q3 | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T09:04:08+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "corrosion-bi3q3", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "corrosion-0", "1": "Slippage", "2": "corrosion", "3": "crack"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T09:04:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for corrosion-bi3q3
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
corrosion-bi3q3
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for corrosion-bi3q3\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncorrosion-bi3q3",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for corrosion-bi3q3\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ncorrosion-bi3q3",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for corrosion-bi3q3\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ncorrosion-bi3q3### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
9f201dac508ef3ed85d57827c3c17dc8eda86bee |
# Dataset Card for gauge-u2lwv
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/gauge-u2lwv
- **Point of Contact:** [email protected]
### Dataset Summary
gauge-u2lwv
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/gauge-u2lwv
### Citation Information
```
@misc{ gauge-u2lwv,
title = { gauge u2lwv Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/gauge-u2lwv } },
url = { https://universe.roboflow.com/object-detection/gauge-u2lwv },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/gauge-u2lwv | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T09:04:51+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "gauge-u2lwv", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "gauge", "1": "gauges", "2": "numbers"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T09:05:04+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for gauge-u2lwv
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
gauge-u2lwv
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for gauge-u2lwv\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ngauge-u2lwv",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for gauge-u2lwv\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ngauge-u2lwv",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
26,
22,
14,
33,
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6,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for gauge-u2lwv\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ngauge-u2lwv### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
d6d339c4268aac972d9527869e66f6b4ec3d424b |
# Dataset Card for halo-infinite-angel-videogame
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame
- **Point of Contact:** [email protected]
### Dataset Summary
halo-infinite-angel-videogame
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame
### Citation Information
```
@misc{ halo-infinite-angel-videogame,
title = { halo infinite angel videogame Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame } },
url = { https://universe.roboflow.com/object-detection/halo-infinite-angel-videogame },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/halo-infinite-angel-videogame | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T09:07:44+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "halo-infinite-angel-videogame", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "halo-infinite-angel-videogame", "1": "enemy", "2": "enemy-head", "3": "friendly", "4": "friendly-head"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T09:07:59+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for halo-infinite-angel-videogame
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
halo-infinite-angel-videogame
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for halo-infinite-angel-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nhalo-infinite-angel-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for halo-infinite-angel-videogame\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nhalo-infinite-angel-videogame",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
29,
22,
17,
33,
5,
6,
20,
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5,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for halo-infinite-angel-videogame\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nhalo-infinite-angel-videogame### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
cf9603951e2a38c55a1737d60587d53e72e15fce |
# Dataset Card for insects-mytwu
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/insects-mytwu
- **Point of Contact:** [email protected]
### Dataset Summary
insects-mytwu
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/insects-mytwu
### Citation Information
```
@misc{ insects-mytwu,
title = { insects mytwu Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/insects-mytwu } },
url = { https://universe.roboflow.com/object-detection/insects-mytwu },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/insects-mytwu | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T09:08:00+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "insects-mytwu", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "insects", "1": "army worm", "2": "legume blister beetle", "3": "red spider", "4": "rice gall midge", "5": "rice leaf roller", "6": "rice leafhopper", "7": "rice water weevil", "8": "wheat phloeothrips", "9": "white backed plant hopper", "10": "yellow rice borer"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T09:08:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for insects-mytwu
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
insects-mytwu
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for insects-mytwu\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ninsects-mytwu",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for insects-mytwu\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\ninsects-mytwu",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
24,
22,
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33,
5,
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233,
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for insects-mytwu\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\ninsects-mytwu### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
931d13bd489d6c7508450c5b24c8906b2539169c |
# Dataset Card for street-work
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/street-work
- **Point of Contact:** [email protected]
### Dataset Summary
street-work
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/street-work
### Citation Information
```
@misc{ street-work,
title = { street work Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/street-work } },
url = { https://universe.roboflow.com/object-detection/street-work },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | Francesco/street-work | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
]
| 2023-03-30T09:12:20+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["object-detection"], "task_ids": [], "pretty_name": "street-work", "dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "objects", "sequence": [{"name": "id", "dtype": "int64"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "category", "dtype": {"class_label": {"names": {"0": "street-work-items", "1": "Cone", "2": "Face_Shield", "3": "Gloves", "4": "Goggles", "5": "Head", "6": "Helmet", "7": "No glasses", "8": "No gloves"}}}}]}]}, "tags": ["rf100"]} | 2023-03-30T09:12:43+00:00 | []
| [
"en"
]
| TAGS
#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us
|
# Dataset Card for street-work
The original COCO dataset is stored at 'URL'
## Dataset Description
- Homepage: URL
- Point of Contact: francesco.zuppichini@URL
### Dataset Summary
street-work
### Supported Tasks and Leaderboards
- 'object-detection': The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
### Data Fields
- 'image': the image id
- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0]["image"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '"image"' column, *i.e.* 'dataset[0]["image"]' should always be preferred over 'dataset["image"][0]'
- 'width': the image width
- 'height': the image height
- 'objects': a dictionary containing bounding box metadata for the objects present on the image
- 'id': the annotation id
- 'area': the area of the bounding box
- 'bbox': the object's bounding box (in the coco format)
- 'category': the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage URL
### Contributions
Thanks to @mariosasko for adding this dataset. | [
"# Dataset Card for street-work\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nstreet-work",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
"TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n",
"# Dataset Card for street-work\n\n The original COCO dataset is stored at 'URL'",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL",
"### Dataset Summary\n\nstreet-work",
"### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.",
"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances\n\nA data point comprises an image and its object annotations.",
"### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.",
"#### Who are the annotators?\n\nAnnotators are Roboflow users",
"## Additional Information",
"### Licensing Information\n\nSee original homepage URL",
"### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
| [
78,
21,
22,
9,
33,
5,
6,
20,
233,
16,
5,
11,
17
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| [
"passage: TAGS\n#task_categories-object-detection #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc #rf100 #region-us \n# Dataset Card for street-work\n\n The original COCO dataset is stored at 'URL'## Dataset Description\n\n- Homepage: URL\n- Point of Contact: francesco.zuppichini@URL### Dataset Summary\n\nstreet-work### Supported Tasks and Leaderboards\n\n- 'object-detection': The dataset can be used to train a model for Object Detection.### Languages\n\nEnglish## Dataset Structure### Data Instances\n\nA data point comprises an image and its object annotations.### Data Fields\n\n- 'image': the image id\n- 'image': 'PIL.Image.Image' object containing the image. Note that when accessing the image column: 'dataset[0][\"image\"]' the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the '\"image\"' column, *i.e.* 'dataset[0][\"image\"]' should always be preferred over 'dataset[\"image\"][0]'\n- 'width': the image width\n- 'height': the image height\n- 'objects': a dictionary containing bounding box metadata for the objects present on the image\n - 'id': the annotation id\n - 'area': the area of the bounding box\n - 'bbox': the object's bounding box (in the coco format)\n - 'category': the object's category.#### Who are the annotators?\n\nAnnotators are Roboflow users## Additional Information### Licensing Information\n\nSee original homepage URL### Contributions\n\nThanks to @mariosasko for adding this dataset."
]
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|
c84d392ce4c75a6081a14f677f5459f17e0ae61b |
### Suomi-24-toxicity-annotated
This dataset includes comments from Suomi24 sampled using predictions from a toxicity classifier. The comments were taken in intervals for each label. The process of sampling emphasized difficult borderline cases. 500 comments were sampled for each label.
The annotation process used the labels from Perspective, used e.g. for `TurkuNLP/wikipedia-toxicity-data-fi`.
Instead of multi-label, we annotated each comment only for one label, although a couple comments appear in two labels.
Process of annotation included initial annotation of 100-200 comments followed by a discussion and final annotations. Raw data can be found from [here](https://github.com/TurkuNLP/toxicity-classifier/tree/main/annotations/raw_annotations).
Examples that made it to the dataset are ones that had unanimous agreement or were resolved through discussion.
### Citing
To cite this dataset use the following bibtex.
```
@inproceedings{eskelinen-etal-2023-toxicity,
title = "Toxicity Detection in {F}innish Using Machine Translation",
author = "Eskelinen, Anni and
Silvala, Laura and
Ginter, Filip and
Pyysalo, Sampo and
Laippala, Veronika",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.68",
pages = "685--697",
abstract = "Due to the popularity of social media platforms and the sheer amount of user-generated content online, the automatic detection of toxic language has become crucial in the creation of a friendly and safe digital space. Previous work has been mostly focusing on English leaving many lower-resource languages behind. In this paper, we present novel resources for toxicity detection in Finnish by introducing two new datasets, a machine translated toxicity dataset for Finnish based on the widely used English Jigsaw dataset and a smaller test set of Suomi24 discussion forum comments originally written in Finnish and manually annotated following the definitions of the labels that were used to annotate the Jigsaw dataset. We show that machine translating the training data to Finnish provides better toxicity detection results than using the original English training data and zero-shot cross-lingual transfer with XLM-R, even with our newly annotated dataset from Suomi24.",
}
```
## Label definitions taken from Perspective API
THREAT: Describes an intention to inflict pain, injury, or violence against an individual or group.
THREATENING: Language that is threatening or encouraging violence or harm, including self-harm.
PROFANITY: Swear words, curse words, or other obscene or profane language.
INSULT: Insulting, inflammatory, or negative comment towards a person or a group of people. Such comments are not necessarily identity specific.
IDENTITY ATTACK: Negative or hateful comments targeting someone because of their identity.
TOXICITY: A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion.
SEVERE TOXICITY: A very hateful, aggressive, disrespectful comment or otherwise very likely to make a user leave a discussion or give up on sharing their perspective. This attribute is much less sensitive to more mild forms of toxicity, such as comments that include positive uses of curse words.
## Guidelines used for annotation:
### Obscene
swearwords, including mild expletives and misspelled, masked, or other variations
sexually explicit words/terminology that are not topically or contextually appropriate
### Threat
suicidal or self-harm comments, incitement to violence or self-harm, hypothetical situations and wishing harm to somebody
comments that are very unlikely to happen if not marked clearly as sarcasm
only threats towards people are annotated as threat
threats made by somebody else other than the writer NOT included
counterfactuals statements NOT included <!--- as in "if I was there I would have..." --->
### Insult
terms that are insulting towards groups of people (also in identity attack)
insults against political groups, e.g. "vitun demari/suvakki/persu" -> "fucking liberal/conservative etc." <!--- I made this decision here.. --->
negative insulting comments towards oneself, things other than people and hypothetical situations NOT included
<!--- PROBLEM: use of racist or rapist if true, target not clear --->
### Identity attack
comments that have no negative language but are still clearly negative
negative statements towards political groups or groups that nobody self-identifies with are NOT included (unless an insult)
### Toxicity
unreasonably expressed negative comments regardless of the target present and whether the target is known or not
mild or humoristic swearwords are NOT included
positive or neutral sexually explicit comments are NOT included
### Severe toxicity
comments that include only sexually explicit content
only one severely toxic element is needed to have this label and a comment is severely toxic even if the comment contains substantive content
target does not need to be present nor does the target matter
## Inter-annotator agreement:
| Label | Initial (unanimous) | After discussion (unanimous) | Initial (at least 2/3) | After discussion (at least 2/3) |
|------ | ------------------- | ---------------------------- | ---------------------- | ------------------------------- |
| identity attack | 54,5 % | 66,6 % | 92 % | 93,6 % |
| insult | 47,5 % | 49,6 % | 94,5 % | 95,6 % |
| severe toxicity | 63 % | 66 % | 92 % | 96,6 % |
| threat | 82 % | 80,3 % | 98 % | 97,3 % |
| toxicity | 58 % | 54 % | 93 % | 89,6 % |
| obscene | 69 % | 62 % | 97 % | 96 % |
## Evaluation results
Evaluation results from using `TurkuNLP/bert-large-finnish-cased-toxicity`.
| Label | Precision | Recall | F1 |
|------ | ------------------- | ---------------------------- | ---------------------- |
| identity attack | 73,2 | 32 | 44,6 |
| insult | 59,4 | 646,8 | 52,4 |
| severe toxicity | 12 | 28,6 | 16,9 |
| threat | 32,4 | 28,6 | 30,4 |
| toxicity | 60,4 | 79,2 | 68,5 |
| obscene | 64,5 | 82,4 | 72,3 |
| OVERALL | 57,4 | 58,9 | 51,1 |
| OVERALL weighted by original sample counts | 55,5 | 65,5 | 60,1 |
## Licensing Information
Contents of this repository are distributed under the
[Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
Copyright of the dataset contents belongs to the original copyright holders. | TurkuNLP/Suomi24-toxicity-annotated | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:fi",
"license:cc-by-sa-4.0",
"toxicity",
"region:us"
]
| 2023-03-30T10:25:13+00:00 | {"language": ["fi"], "license": "cc-by-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "tags": ["toxicity"]} | 2023-06-02T12:04:21+00:00 | []
| [
"fi"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Finnish #license-cc-by-sa-4.0 #toxicity #region-us
| ### Suomi-24-toxicity-annotated
This dataset includes comments from Suomi24 sampled using predictions from a toxicity classifier. The comments were taken in intervals for each label. The process of sampling emphasized difficult borderline cases. 500 comments were sampled for each label.
The annotation process used the labels from Perspective, used e.g. for 'TurkuNLP/wikipedia-toxicity-data-fi'.
Instead of multi-label, we annotated each comment only for one label, although a couple comments appear in two labels.
Process of annotation included initial annotation of 100-200 comments followed by a discussion and final annotations. Raw data can be found from here.
Examples that made it to the dataset are ones that had unanimous agreement or were resolved through discussion.
### Citing
To cite this dataset use the following bibtex.
Label definitions taken from Perspective API
--------------------------------------------
THREAT: Describes an intention to inflict pain, injury, or violence against an individual or group.
THREATENING: Language that is threatening or encouraging violence or harm, including self-harm.
PROFANITY: Swear words, curse words, or other obscene or profane language.
INSULT: Insulting, inflammatory, or negative comment towards a person or a group of people. Such comments are not necessarily identity specific.
IDENTITY ATTACK: Negative or hateful comments targeting someone because of their identity.
TOXICITY: A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion.
SEVERE TOXICITY: A very hateful, aggressive, disrespectful comment or otherwise very likely to make a user leave a discussion or give up on sharing their perspective. This attribute is much less sensitive to more mild forms of toxicity, such as comments that include positive uses of curse words.
Guidelines used for annotation:
-------------------------------
### Obscene
swearwords, including mild expletives and misspelled, masked, or other variations
sexually explicit words/terminology that are not topically or contextually appropriate
### Threat
suicidal or self-harm comments, incitement to violence or self-harm, hypothetical situations and wishing harm to somebody
comments that are very unlikely to happen if not marked clearly as sarcasm
only threats towards people are annotated as threat
threats made by somebody else other than the writer NOT included
counterfactuals statements NOT included
### Insult
terms that are insulting towards groups of people (also in identity attack)
insults against political groups, e.g. "vitun demari/suvakki/persu" -> "fucking liberal/conservative etc."
negative insulting comments towards oneself, things other than people and hypothetical situations NOT included
### Identity attack
comments that have no negative language but are still clearly negative
negative statements towards political groups or groups that nobody self-identifies with are NOT included (unless an insult)
### Toxicity
unreasonably expressed negative comments regardless of the target present and whether the target is known or not
mild or humoristic swearwords are NOT included
positive or neutral sexually explicit comments are NOT included
### Severe toxicity
comments that include only sexually explicit content
only one severely toxic element is needed to have this label and a comment is severely toxic even if the comment contains substantive content
target does not need to be present nor does the target matter
Inter-annotator agreement:
--------------------------
Evaluation results
------------------
Evaluation results from using 'TurkuNLP/bert-large-finnish-cased-toxicity'.
Licensing Information
---------------------
Contents of this repository are distributed under the
Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).
Copyright of the dataset contents belongs to the original copyright holders.
| [
"### Suomi-24-toxicity-annotated\n\n\nThis dataset includes comments from Suomi24 sampled using predictions from a toxicity classifier. The comments were taken in intervals for each label. The process of sampling emphasized difficult borderline cases. 500 comments were sampled for each label. \n\nThe annotation process used the labels from Perspective, used e.g. for 'TurkuNLP/wikipedia-toxicity-data-fi'. \n\nInstead of multi-label, we annotated each comment only for one label, although a couple comments appear in two labels. \n\nProcess of annotation included initial annotation of 100-200 comments followed by a discussion and final annotations. Raw data can be found from here.\n\n\nExamples that made it to the dataset are ones that had unanimous agreement or were resolved through discussion.",
"### Citing\n\n\nTo cite this dataset use the following bibtex.\n\n\nLabel definitions taken from Perspective API\n--------------------------------------------\n\n\nTHREAT: Describes an intention to inflict pain, injury, or violence against an individual or group.\nTHREATENING: Language that is threatening or encouraging violence or harm, including self-harm.\n\n\nPROFANITY: Swear words, curse words, or other obscene or profane language.\n\n\nINSULT: Insulting, inflammatory, or negative comment towards a person or a group of people. Such comments are not necessarily identity specific.\n\n\nIDENTITY ATTACK: Negative or hateful comments targeting someone because of their identity.\n\n\nTOXICITY: A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion.\n\n\nSEVERE TOXICITY: A very hateful, aggressive, disrespectful comment or otherwise very likely to make a user leave a discussion or give up on sharing their perspective. This attribute is much less sensitive to more mild forms of toxicity, such as comments that include positive uses of curse words.\n\n\nGuidelines used for annotation:\n-------------------------------",
"### Obscene\n\n\nswearwords, including mild expletives and misspelled, masked, or other variations \n\nsexually explicit words/terminology that are not topically or contextually appropriate",
"### Threat\n\n\nsuicidal or self-harm comments, incitement to violence or self-harm, hypothetical situations and wishing harm to somebody \n\ncomments that are very unlikely to happen if not marked clearly as sarcasm \n\nonly threats towards people are annotated as threat\n\n\nthreats made by somebody else other than the writer NOT included \n\ncounterfactuals statements NOT included",
"### Insult\n\n\nterms that are insulting towards groups of people (also in identity attack) \n\ninsults against political groups, e.g. \"vitun demari/suvakki/persu\" -> \"fucking liberal/conservative etc.\"\n\n\nnegative insulting comments towards oneself, things other than people and hypothetical situations NOT included",
"### Identity attack\n\n\ncomments that have no negative language but are still clearly negative\n\n\nnegative statements towards political groups or groups that nobody self-identifies with are NOT included (unless an insult)",
"### Toxicity\n\n\nunreasonably expressed negative comments regardless of the target present and whether the target is known or not \n\nmild or humoristic swearwords are NOT included \n\npositive or neutral sexually explicit comments are NOT included",
"### Severe toxicity\n\n\ncomments that include only sexually explicit content \n\nonly one severely toxic element is needed to have this label and a comment is severely toxic even if the comment contains substantive content \n\ntarget does not need to be present nor does the target matter\n\n\nInter-annotator agreement:\n--------------------------\n\n\n\nEvaluation results\n------------------\n\n\nEvaluation results from using 'TurkuNLP/bert-large-finnish-cased-toxicity'.\n\n\n\nLicensing Information\n---------------------\n\n\nContents of this repository are distributed under the\nCreative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).\nCopyright of the dataset contents belongs to the original copyright holders."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Finnish #license-cc-by-sa-4.0 #toxicity #region-us \n",
"### Suomi-24-toxicity-annotated\n\n\nThis dataset includes comments from Suomi24 sampled using predictions from a toxicity classifier. The comments were taken in intervals for each label. The process of sampling emphasized difficult borderline cases. 500 comments were sampled for each label. \n\nThe annotation process used the labels from Perspective, used e.g. for 'TurkuNLP/wikipedia-toxicity-data-fi'. \n\nInstead of multi-label, we annotated each comment only for one label, although a couple comments appear in two labels. \n\nProcess of annotation included initial annotation of 100-200 comments followed by a discussion and final annotations. Raw data can be found from here.\n\n\nExamples that made it to the dataset are ones that had unanimous agreement or were resolved through discussion.",
"### Citing\n\n\nTo cite this dataset use the following bibtex.\n\n\nLabel definitions taken from Perspective API\n--------------------------------------------\n\n\nTHREAT: Describes an intention to inflict pain, injury, or violence against an individual or group.\nTHREATENING: Language that is threatening or encouraging violence or harm, including self-harm.\n\n\nPROFANITY: Swear words, curse words, or other obscene or profane language.\n\n\nINSULT: Insulting, inflammatory, or negative comment towards a person or a group of people. Such comments are not necessarily identity specific.\n\n\nIDENTITY ATTACK: Negative or hateful comments targeting someone because of their identity.\n\n\nTOXICITY: A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion.\n\n\nSEVERE TOXICITY: A very hateful, aggressive, disrespectful comment or otherwise very likely to make a user leave a discussion or give up on sharing their perspective. This attribute is much less sensitive to more mild forms of toxicity, such as comments that include positive uses of curse words.\n\n\nGuidelines used for annotation:\n-------------------------------",
"### Obscene\n\n\nswearwords, including mild expletives and misspelled, masked, or other variations \n\nsexually explicit words/terminology that are not topically or contextually appropriate",
"### Threat\n\n\nsuicidal or self-harm comments, incitement to violence or self-harm, hypothetical situations and wishing harm to somebody \n\ncomments that are very unlikely to happen if not marked clearly as sarcasm \n\nonly threats towards people are annotated as threat\n\n\nthreats made by somebody else other than the writer NOT included \n\ncounterfactuals statements NOT included",
"### Insult\n\n\nterms that are insulting towards groups of people (also in identity attack) \n\ninsults against political groups, e.g. \"vitun demari/suvakki/persu\" -> \"fucking liberal/conservative etc.\"\n\n\nnegative insulting comments towards oneself, things other than people and hypothetical situations NOT included",
"### Identity attack\n\n\ncomments that have no negative language but are still clearly negative\n\n\nnegative statements towards political groups or groups that nobody self-identifies with are NOT included (unless an insult)",
"### Toxicity\n\n\nunreasonably expressed negative comments regardless of the target present and whether the target is known or not \n\nmild or humoristic swearwords are NOT included \n\npositive or neutral sexually explicit comments are NOT included",
"### Severe toxicity\n\n\ncomments that include only sexually explicit content \n\nonly one severely toxic element is needed to have this label and a comment is severely toxic even if the comment contains substantive content \n\ntarget does not need to be present nor does the target matter\n\n\nInter-annotator agreement:\n--------------------------\n\n\n\nEvaluation results\n------------------\n\n\nEvaluation results from using 'TurkuNLP/bert-large-finnish-cased-toxicity'.\n\n\n\nLicensing Information\n---------------------\n\n\nContents of this repository are distributed under the\nCreative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).\nCopyright of the dataset contents belongs to the original copyright holders."
]
| [
49,
182,
261,
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150
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| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Finnish #license-cc-by-sa-4.0 #toxicity #region-us \n### Suomi-24-toxicity-annotated\n\n\nThis dataset includes comments from Suomi24 sampled using predictions from a toxicity classifier. The comments were taken in intervals for each label. The process of sampling emphasized difficult borderline cases. 500 comments were sampled for each label. \n\nThe annotation process used the labels from Perspective, used e.g. for 'TurkuNLP/wikipedia-toxicity-data-fi'. \n\nInstead of multi-label, we annotated each comment only for one label, although a couple comments appear in two labels. \n\nProcess of annotation included initial annotation of 100-200 comments followed by a discussion and final annotations. Raw data can be found from here.\n\n\nExamples that made it to the dataset are ones that had unanimous agreement or were resolved through discussion.### Citing\n\n\nTo cite this dataset use the following bibtex.\n\n\nLabel definitions taken from Perspective API\n--------------------------------------------\n\n\nTHREAT: Describes an intention to inflict pain, injury, or violence against an individual or group.\nTHREATENING: Language that is threatening or encouraging violence or harm, including self-harm.\n\n\nPROFANITY: Swear words, curse words, or other obscene or profane language.\n\n\nINSULT: Insulting, inflammatory, or negative comment towards a person or a group of people. Such comments are not necessarily identity specific.\n\n\nIDENTITY ATTACK: Negative or hateful comments targeting someone because of their identity.\n\n\nTOXICITY: A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion.\n\n\nSEVERE TOXICITY: A very hateful, aggressive, disrespectful comment or otherwise very likely to make a user leave a discussion or give up on sharing their perspective. This attribute is much less sensitive to more mild forms of toxicity, such as comments that include positive uses of curse words.\n\n\nGuidelines used for annotation:\n-------------------------------"
]
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|
9fc30b578cedaec69e47302df72cf00feed7c8c4 | # StarCoder Training Dataset
## Dataset description
This is the dataset used for training [StarCoder](https://huggingface.co/bigcode/starcoder) and [StarCoderBase](https://huggingface.co/bigcode/starcoderbase). It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,
and 32GB of GitHub commits, which is approximately 250 Billion tokens.
## Dataset creation
The creation and filtering of The Stack is explained in the [original dataset](https://huggingface.co/datasets/bigcode/the-stack-dedup), we additionally decontaminate and clean all 86 programming
languages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our [Paper: 💫 StarCoder, May The Source Be With You](https://drive.google.com/file/d/1cN-b9GnWtHzQRoE7M7gAEyivY0kl4BYs/view)
## How to use the dataset
```python
from datasets import load_dataset
# to load python for example
ds = load_dataset("bigcode/starcoderdata", data_dir="python", split="train")
```
GitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories.
````
jupyter-scripts-dedup-filtered
jupyter-structured-clean-dedup
github-issues-filtered-structured
git-commits-cleaned
````
| bigcode/starcoderdata | [
"task_categories:text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:other",
"region:us"
]
| 2023-03-30T11:02:21+00:00 | {"annotations_creators": [], "language_creators": ["crowdsourced", "expert-generated"], "language": ["code"], "license": ["other"], "multilinguality": ["multilingual"], "size_categories": ["unknown"], "source_datasets": [], "task_categories": ["text-generation"], "pretty_name": "The-Stack", "extra_gated_prompt": "## Terms of Use for The Stack\n\nThe Stack dataset is a collection of source code in over 300 programming languages. We ask that you read and acknowledge the following points before using the dataset:\n1. The Stack is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.\n2. The Stack is regularly updated to enact validated data removal requests. By clicking on \"Access repository\", you agree to update your own version of The Stack to the most recent usable version specified by the maintainers in [the following thread](https://huggingface.co/datasets/bigcode/the-stack/discussions/7). If you have questions about dataset versions and allowed uses, please also ask them in the dataset\u2019s [community discussions](https://huggingface.co/datasets/bigcode/the-stack/discussions/new). We will also notify users via email when the latest usable version changes.\n3. To host, share, or otherwise provide access to The Stack dataset, you must include [these Terms of Use](https://huggingface.co/datasets/bigcode/the-stack#terms-of-use-for-the-stack) and require users to agree to it.\n\nBy clicking on \"Access repository\" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.\n ", "extra_gated_fields": {"Email": "text", "I have read the License and agree with its terms": "checkbox"}} | 2023-05-16T09:05:48+00:00 | []
| [
"code"
]
| TAGS
#task_categories-text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-other #region-us
| # StarCoder Training Dataset
## Dataset description
This is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,
and 32GB of GitHub commits, which is approximately 250 Billion tokens.
## Dataset creation
The creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate and clean all 86 programming
languages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our Paper: StarCoder, May The Source Be With You
## How to use the dataset
GitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories.
'
| [
"# StarCoder Training Dataset",
"## Dataset description\nThis is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,\nand 32GB of GitHub commits, which is approximately 250 Billion tokens.",
"## Dataset creation\nThe creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate and clean all 86 programming\nlanguages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our Paper: StarCoder, May The Source Be With You",
"## How to use the dataset\n\n\nGitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories. \n'"
]
| [
"TAGS\n#task_categories-text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-other #region-us \n",
"# StarCoder Training Dataset",
"## Dataset description\nThis is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,\nand 32GB of GitHub commits, which is approximately 250 Billion tokens.",
"## Dataset creation\nThe creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate and clean all 86 programming\nlanguages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our Paper: StarCoder, May The Source Be With You",
"## How to use the dataset\n\n\nGitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories. \n'"
]
| [
65,
7,
84,
94,
63
]
| [
"passage: TAGS\n#task_categories-text-generation #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-multilingual #size_categories-unknown #language-code #license-other #region-us \n# StarCoder Training Dataset## Dataset description\nThis is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,\nand 32GB of GitHub commits, which is approximately 250 Billion tokens.## Dataset creation\nThe creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate and clean all 86 programming\nlanguages in the dataset, in addition to GitHub issues, Jupyter Notebooks and GitHub commits. We also apply near-deduplication and remove PII, all details are mentionned in our Paper: StarCoder, May The Source Be With You## How to use the dataset\n\n\nGitHub issues, GitHub commits and Jupyter notebooks subsets have different columns from the rest so loading the entire dataset at once may fail, we suggest loading programming languages separatly from these categories. \n'"
]
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|
e738267573fcd516b79c02de81622b1b415a30b5 | # Dataset Card for "massive_social-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_social-de-DE | [
"region:us"
]
| 2023-03-30T11:37:11+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 129790, "num_examples": 391}, {"name": "validation", "num_bytes": 22472, "num_examples": 68}, {"name": "test", "num_bytes": 34107, "num_examples": 106}], "download_size": 70328, "dataset_size": 186369}} | 2023-03-30T11:48:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_social-de-DE"
More Information needed | [
"# Dataset Card for \"massive_social-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_social-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_social-de-DE\"\n\nMore Information needed"
]
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|
ae57f56ab7175695886469c18d8fdf4a832435bf | # Dataset Card for "massive_transport-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_transport-de-DE | [
"region:us"
]
| 2023-03-30T11:37:28+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 191209, "num_examples": 571}, {"name": "validation", "num_bytes": 36883, "num_examples": 110}, {"name": "test", "num_bytes": 41087, "num_examples": 124}], "download_size": 80546, "dataset_size": 269179}} | 2023-03-30T11:48:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_transport-de-DE"
More Information needed | [
"# Dataset Card for \"massive_transport-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_transport-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_transport-de-DE\"\n\nMore Information needed"
]
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|
9fea3243b565faf223fb23923cec0e2cd28d46bd | # Dataset Card for "massive_calendar-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_calendar-de-DE | [
"region:us"
]
| 2023-03-30T11:37:45+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 553408, "num_examples": 1688}, {"name": "validation", "num_bytes": 91646, "num_examples": 280}, {"name": "test", "num_bytes": 132217, "num_examples": 402}], "download_size": 196802, "dataset_size": 777271}} | 2023-03-30T11:49:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_calendar-de-DE"
More Information needed | [
"# Dataset Card for \"massive_calendar-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_calendar-de-DE\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_calendar-de-DE\"\n\nMore Information needed"
]
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|
6a4451a0b5e140bd04dc596d6aa542f0896b51e2 | # Dataset Card for "massive_play-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_play-de-DE | [
"region:us"
]
| 2023-03-30T11:38:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 398096, "num_examples": 1377}, {"name": "validation", "num_bytes": 73925, "num_examples": 260}, {"name": "test", "num_bytes": 111797, "num_examples": 387}], "download_size": 152587, "dataset_size": 583818}} | 2023-03-30T11:49:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_play-de-DE"
More Information needed | [
"# Dataset Card for \"massive_play-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_play-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_play-de-DE\"\n\nMore Information needed"
]
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|
df751e2f8e95d950506292dfae8306b284ea9a53 | # Dataset Card for "massive_news-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_news-de-DE | [
"region:us"
]
| 2023-03-30T11:38:20+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 147499, "num_examples": 503}, {"name": "validation", "num_bytes": 25026, "num_examples": 82}, {"name": "test", "num_bytes": 36859, "num_examples": 124}], "download_size": 69773, "dataset_size": 209384}} | 2023-03-30T11:49:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_news-de-DE"
More Information needed | [
"# Dataset Card for \"massive_news-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_news-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_news-de-DE\"\n\nMore Information needed"
]
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|
97e4a1233698c58bd7de2467631a21aaac53b569 | # Dataset Card for "massive_datetime-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_datetime-de-DE | [
"region:us"
]
| 2023-03-30T11:38:37+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 114125, "num_examples": 402}, {"name": "validation", "num_bytes": 20737, "num_examples": 73}, {"name": "test", "num_bytes": 29494, "num_examples": 103}], "download_size": 57424, "dataset_size": 164356}} | 2023-03-30T11:50:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_datetime-de-DE"
More Information needed | [
"# Dataset Card for \"massive_datetime-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_datetime-de-DE\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_datetime-de-DE\"\n\nMore Information needed"
]
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|
7cf89961a7993ef4d1a6017002ac44d70c3e2216 | # Dataset Card for "massive_recommendation-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_recommendation-de-DE | [
"region:us"
]
| 2023-03-30T11:38:54+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 137660, "num_examples": 433}, {"name": "validation", "num_bytes": 22189, "num_examples": 69}, {"name": "test", "num_bytes": 31179, "num_examples": 94}], "download_size": 67251, "dataset_size": 191028}} | 2023-03-30T11:50:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_recommendation-de-DE"
More Information needed | [
"# Dataset Card for \"massive_recommendation-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_recommendation-de-DE\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_recommendation-de-DE\"\n\nMore Information needed"
]
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|
5b873761a6d28a5a971cc47d6677945880e27056 | # Dataset Card for "massive_email-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_email-de-DE | [
"region:us"
]
| 2023-03-30T11:39:10+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 285273, "num_examples": 953}, {"name": "validation", "num_bytes": 46537, "num_examples": 157}, {"name": "test", "num_bytes": 79990, "num_examples": 271}], "download_size": 116461, "dataset_size": 411800}} | 2023-03-30T11:51:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_email-de-DE"
More Information needed | [
"# Dataset Card for \"massive_email-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_email-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_email-de-DE\"\n\nMore Information needed"
]
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|
34fee651d175bde5d7285d6d8f3f48ab154ebe7f | # Dataset Card for "massive_iot-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_iot-de-DE | [
"region:us"
]
| 2023-03-30T11:39:27+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 212741, "num_examples": 769}, {"name": "validation", "num_bytes": 31932, "num_examples": 118}, {"name": "test", "num_bytes": 60007, "num_examples": 220}], "download_size": 84035, "dataset_size": 304680}} | 2023-03-30T11:51:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_iot-de-DE"
More Information needed | [
"# Dataset Card for \"massive_iot-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_iot-de-DE\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_iot-de-DE\"\n\nMore Information needed"
]
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|
e8c86661cde54a13c52fdc07e5a2876ffbff44b5 | # Dataset Card for "massive_general-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_general-de-DE | [
"region:us"
]
| 2023-03-30T11:39:44+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 171110, "num_examples": 652}, {"name": "validation", "num_bytes": 31311, "num_examples": 122}, {"name": "test", "num_bytes": 49862, "num_examples": 189}], "download_size": 90317, "dataset_size": 252283}} | 2023-03-30T11:51:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_general-de-DE"
More Information needed | [
"# Dataset Card for \"massive_general-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_general-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_general-de-DE\"\n\nMore Information needed"
]
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|
50596ed705995cc776b244d8c9806be00cbae2d1 | # Dataset Card for "massive_audio-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_audio-de-DE | [
"region:us"
]
| 2023-03-30T11:40:01+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 71359, "num_examples": 290}, {"name": "validation", "num_bytes": 8438, "num_examples": 35}, {"name": "test", "num_bytes": 14871, "num_examples": 62}], "download_size": 42899, "dataset_size": 94668}} | 2023-03-30T11:52:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_audio-de-DE"
More Information needed | [
"# Dataset Card for \"massive_audio-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_audio-de-DE\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_audio-de-DE\"\n\nMore Information needed"
]
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|
8e655834c57a2fca6f95f611ae972253cd61188c | # Dataset Card for "massive_lists-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_lists-de-DE | [
"region:us"
]
| 2023-03-30T11:40:17+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 143624, "num_examples": 539}, {"name": "validation", "num_bytes": 29446, "num_examples": 112}, {"name": "test", "num_bytes": 37628, "num_examples": 142}], "download_size": 69778, "dataset_size": 210698}} | 2023-03-30T11:52:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_lists-de-DE"
More Information needed | [
"# Dataset Card for \"massive_lists-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_lists-de-DE\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_lists-de-DE\"\n\nMore Information needed"
]
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|
d295a88c95ac39aab5e440fcb84c32d5b67a02ff | # Dataset Card for "massive_qa-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_qa-de-DE | [
"region:us"
]
| 2023-03-30T11:40:34+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 329537, "num_examples": 1183}, {"name": "validation", "num_bytes": 59481, "num_examples": 214}, {"name": "test", "num_bytes": 79960, "num_examples": 288}], "download_size": 141433, "dataset_size": 468978}} | 2023-03-30T11:53:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_qa-de-DE"
More Information needed | [
"# Dataset Card for \"massive_qa-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_qa-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_qa-de-DE\"\n\nMore Information needed"
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|
4a36ec73548b9df753c7952e3850d2801ca1a587 | # Dataset Card for "massive_cooking-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_cooking-de-DE | [
"region:us"
]
| 2023-03-30T11:40:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 61653, "num_examples": 211}, {"name": "validation", "num_bytes": 12070, "num_examples": 43}, {"name": "test", "num_bytes": 21241, "num_examples": 72}], "download_size": 46458, "dataset_size": 94964}} | 2023-03-30T11:53:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_cooking-de-DE"
More Information needed | [
"# Dataset Card for \"massive_cooking-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_cooking-de-DE\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_cooking-de-DE\"\n\nMore Information needed"
]
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|
c8fd26ef5c204332c50c081c7616291e5130e3a6 | # Dataset Card for "massive_takeaway-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_takeaway-de-DE | [
"region:us"
]
| 2023-03-30T11:41:08+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 85401, "num_examples": 257}, {"name": "validation", "num_bytes": 13974, "num_examples": 44}, {"name": "test", "num_bytes": 18174, "num_examples": 57}], "download_size": 51358, "dataset_size": 117549}} | 2023-03-30T11:54:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_takeaway-de-DE"
More Information needed | [
"# Dataset Card for \"massive_takeaway-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_takeaway-de-DE\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_takeaway-de-DE\"\n\nMore Information needed"
]
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|
a0b8310ae8f86702010304955e38abfc7531c722 | # Dataset Card for "massive_music-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | fathyshalab/massive_music-de-DE | [
"region:us"
]
| 2023-03-30T11:41:24+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "scenario", "dtype": {"class_label": {"names": {"0": "social", "1": "transport", "2": "calendar", "3": "play", "4": "news", "5": "datetime", "6": "recommendation", "7": "email", "8": "iot", "9": "general", "10": "audio", "11": "lists", "12": "qa", "13": "cooking", "14": "takeaway", "15": "music", "16": "alarm", "17": "weather"}}}}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "datetime_query", "1": "iot_hue_lightchange", "2": "transport_ticket", "3": "takeaway_query", "4": "qa_stock", "5": "general_greet", "6": "recommendation_events", "7": "music_dislikeness", "8": "iot_wemo_off", "9": "cooking_recipe", "10": "qa_currency", "11": "transport_traffic", "12": "general_quirky", "13": "weather_query", "14": "audio_volume_up", "15": "email_addcontact", "16": "takeaway_order", "17": "email_querycontact", "18": "iot_hue_lightup", "19": "recommendation_locations", "20": "play_audiobook", "21": "lists_createoradd", "22": "news_query", "23": "alarm_query", "24": "iot_wemo_on", "25": "general_joke", "26": "qa_definition", "27": "social_query", "28": "music_settings", "29": "audio_volume_other", "30": "calendar_remove", "31": "iot_hue_lightdim", "32": "calendar_query", "33": "email_sendemail", "34": "iot_cleaning", "35": "audio_volume_down", "36": "play_radio", "37": "cooking_query", "38": "datetime_convert", "39": "qa_maths", "40": "iot_hue_lightoff", "41": "iot_hue_lighton", "42": "transport_query", "43": "music_likeness", "44": "email_query", "45": "play_music", "46": "audio_volume_mute", "47": "social_post", "48": "alarm_set", "49": "qa_factoid", "50": "calendar_set", "51": "play_game", "52": "alarm_remove", "53": "lists_remove", "54": "transport_taxi", "55": "recommendation_movies", "56": "iot_coffee", "57": "music_query", "58": "play_podcasts", "59": "lists_query"}}}}, {"name": "text", "dtype": "string"}, {"name": "annot_utt", "dtype": "string"}, {"name": "worker_id", "dtype": "string"}, {"name": "slot_method", "sequence": [{"name": "slot", "dtype": "string"}, {"name": "method", "dtype": "string"}]}, {"name": "judgments", "sequence": [{"name": "worker_id", "dtype": "string"}, {"name": "intent_score", "dtype": "int8"}, {"name": "slots_score", "dtype": "int8"}, {"name": "grammar_score", "dtype": "int8"}, {"name": "spelling_score", "dtype": "int8"}, {"name": "language_identification", "dtype": "string"}]}, {"name": "label_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 86249, "num_examples": 332}, {"name": "validation", "num_bytes": 14803, "num_examples": 56}, {"name": "test", "num_bytes": 20685, "num_examples": 81}], "download_size": 53750, "dataset_size": 121737}} | 2023-03-30T11:54:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "massive_music-de-DE"
More Information needed | [
"# Dataset Card for \"massive_music-de-DE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"massive_music-de-DE\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"massive_music-de-DE\"\n\nMore Information needed"
]
| [
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|
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